Bibliography

1. Agarwal S, Agrawal R, Deshpande PM, et al. On the computation of multidimensional aggregates. In: Proc 1996 Int Conf Very Large Data Bases (VLDB’96). Bombay, India. Sept. 1996;506–521.

2. Agarwal R, Aggarwal CC, Prasad VVV. A tree projection algorithm for generation of frequent itemsets. J Parallel and Distributed Computing. 2001;61:350–371.

3. Abraham B, Box GEP. Bayesian analysis of some outlier problems in time series. Biometrika. 1979;66:229–248.

4. Albert R, Barabasi A-L. Emergence of scaling in random networks. Science. 1999;286:509–512.

5. Agyemang M, Barker K, Alhajj R. A comprehensive survey of numeric and symbolic outlier mining techniques. Intell Data Anal. 2006;10:521–538.

6. Ankerst M, Breunig M, Kriegel H-P, Sander J. OPTICS: Ordering points to identify the clustering structure. In: Proc 1999 ACM-SIGMOD Int Conf Management of Data (SIGMOD’99). Philadelphia, PA. June 1999;49–60.

7. Almuallim H, Dietterich TG. Learning with many irrelevant features. In: Proc 1991 Nat Conf Artificial Intelligence (AAAI’91). Anaheim, CA. July 1991;547–552.

8. Ankerst M, Elsen C, Ester M, Kriegel H-P. Visual classification: An interactive approach to decision tree construction. In: Proc 1999 Int Conf Knowledge Discovery and Data Mining (KDD’99). San Diego, CA. Aug. 1999;392–396.

9. Ahmed KM, El-Makky NM, Taha Y. A note on “beyond market basket: Generalizing association rules to correlations.”. SIGKDD Explorations. 2000;1:46–48.

10. Anscombe FJ, Guttman I. Rejection of outliers. Technometrics. 1960;2:123–147.

11. Agarwal D. Detecting anomalies in cross-classified streams: A Bayesian approach. Knowl Inf Syst. 2006;11:29–44.

12. Amigó E, Gonzalo J, Artiles J, Verdejo F. A comparison of extrinsic clustering evaluation metrics based on formal constraints. Information Retrieval. 2009;12(4):461–486.

13. Aggarwal CC. Data Streams: Models and Algorithms Kluwer Academic 2006.

14. Agrawal R, Gehrke J, Gunopulos D, Raghavan P. Automatic subspace clustering of high dimensional data for data mining applications. In: Proc 1998 ACM-SIGMOD Int Conf Management of Data (SIGMOD’98). Seattle, WA. June 1998;94–105.

15. Afrati FN, Gionis A, Mannila H. Approximating a collection of frequent sets. In: Proc 2004 ACM SIGKDD Int Conf Knowledge Discovery in Databases (KDD’04). Seattle, WA. Aug. 2004;12–19.

16. Agrawal R, Gupta A, Sarawagi S. Modeling multidimensional databases. In: Proc 1997 Int Conf Data Engineering (ICDE’97). Birmingham, England. Apr. 1997;232–243.

17. Aha D. Tolerating noisy, irrelevant, and novel attributes in instance-based learning algorithms. Int J Man-Machine Studies. 1992;36:267–287.

18. Arabie P, Hubert LJ, De Soete G. Clustering and Classification World Scientific 1996.

19. Aggarwal CC, Han J, Wang J, Yu PS. A framework for clustering evolving data streams. In: Proc 2003 Int Conf Very Large Data Bases (VLDB’03). Berlin, Germany. Sept. 2003;81–92.

20. Aggarwal CC, Han J, Wang J, Yu PS. A framework for projected clustering of high dimensional data streams. In: Proc 2004 Int Conf Very Large Data Bases (VLDB’04). Toronto, Ontario, Canada. Aug. 2004;852–863.

21. Aggarwal CC, Han J, Wang J, Yu PS. On demand classification of data streams. In: Proc 2004 ACM SIGKDD Int Conf Knowledge Discovery in Databases (KDD’04). Seattle, WA. Aug. 2004;503–508.

22. Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. In: Proc 1993 ACM-SIGMOD Int Conf Management of Data (SIGMOD’93). Washington, DC. May 1993;207–216.

23. Anand T, Kahn G. Opportunity explorer: Navigating large databases using knowledge discovery templates. In: Proc AAAI-93 Workshop Knowledge Discovery in Databases. Washington, DC. July 1993;45–51.

24. Aumann Y, Lindell Y. A statistical theory for quantitative association rules. In: Proc 1999 Int Conf Knowledge Discovery and Data Mining (KDD’99). San Diego, CA. Aug. 1999;261–270.

25. Allen BP. Case-based reasoning: Business applications. Communications of the ACM. 1994;37:40–42.

26. Alpaydin E. Introduction to Machine Learning 2nd ed. Cambridge, MA: MIT Press; 2011.

27. Agrawal R, Lin K-I, Sawhney HS, Shim K. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In: Proc 1995 Int Conf Very Large Data Bases (VLDB’95). Zurich, Switzerland. Sept. 1995;490–501.

28. Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo AI. Fast discovery of association rules. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R, eds. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press 1996;307–328.

29. Aoki PM. Generalizing “search” in generalized search trees. In: Proc 1998 Int Conf Data Engineering (ICDE’98). Orlando, FL. Feb. 1998;380–389.

30. Aamodt A, Plazas E. Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications. 1994;7:39–52.

31. Angiulli F, Pizzuti C. Outlier mining in large high-dimensional data sets. IEEE Trans on Knowl and Data Eng. 2005;17:203–215.

32. Aggarwal CC, Procopiuc C, Wolf J, Yu PS, Park J-S. Fast algorithms for projected clustering. In: Proc 1999 ACM-SIGMOD Int Conf Management of Data (SIGMOD’99). Philadelphia, PA. June 1999;61–72.

33. Arora S, Rao S, Vazirani U. Expander flows, geometric embeddings and graph partitioning. J ACM. 2009;56(2):1–37.

34. Agrawal R, Srikant R. Fast algorithm for mining association rules in large databases. In: Research Report RJ 9839. San Jose, CA: IBM Almaden Research Center; June 1994.

35. Agrawal R, Srikant R. Fast algorithms for mining association rules. In: Proc 1994 Int Conf Very Large Data Bases (VLDB’94). Santiago, Chile. Sept. 1994;487–499.

36. Agrawal R, Srikant R. Mining sequential patterns. In: Proc 1995 Int Conf Data Engineering (ICDE’95). Taipei, Taiwan. Mar. 1995;3–14.

37. Agrawal R, Shafer JC. Parallel mining of association rules: Design, implementation, and experience. IEEE Trans Knowledge and Data Engineering. 1996;8:962–969.

38. Agrawal R, Srikant R. Privacy-preserving data mining. In: Proc 2000 ACM-SIGMOD Int Conf Management of Data (SIGMOD’00). Dallas, TX. May 2000;439–450.

39. Allwein E, Shapire R, Singer Y. Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research. 2000;1:113–141.

40. Arthur D, Vassilvitskii S. K-means++: The advantages of careful seeding. In: Proc 2007 ACM-SIAM Symp on Discrete Algorithms (SODA’07). Tokyo. 2007;1027–1035.

41. Avner S. Discovery of comprehensible symbolic rules in a neural network. In: Proc 1995 Int Symp Intelligence in Neural and Biological Systems. Washington, DC. 1995;64–67.

42. Aggarwal CC, Yu PS. A new framework for itemset generation. In: Proc 1998 ACM Symp Principles of Database Systems (PODS’98). Seattle, WA. June 1999;18–24.

43. Aggarwal CC, Yu PS. Outlier detection for high dimensional data. In: Proc 2001 ACM-SIGMOD Int Conf Management of Data (SIGMOD’01). Santa Barbara, CA. May 2001;37–46.

44. Aggarwal CC, Yu PS. Privacy-Preserving Data Mining: Models and Algorithms New York: Springer; 2008.

45. Breslow LA, Aha DW. Simplifying decision trees: A survey. Knowledge Engineering Rev. 1997;12:1–40.

46. Bayardo RJ. Efficiently mining long patterns from databases. In: Proc 1998 ACM-SIGMOD Int Conf Management of Data (SIGMOD’98). Seattle, WA. June 1998;85–93.

47. Bagga A, Baldwin B. Entity-based cross-document coreferencing using the vector space model. In: Proc 1998 Annual Meeting of the Association for Computational Linguistics and Int Conf Computational Linguistics (COLING-ACL’98). Montreal, Quebec, Canada. Aug. 1998.

48. Baldi P, Brunak S. Bioinformatics: The Machine Learning Approach 2nd ed. Cambridge, MA: MIT Press; 2001.

49. Borgelt C, Berthold MR. Mining molecular fragments: Finding relevant substructures of molecules. In: Proc 2002 Int Conf Data Mining (ICDM’02). Maebashi, Japan. Dec. 2002;211–218.

50. Babcock B, Babu S, Datar M, Motwani R, Widom J. Models and issues in data stream systems. In: Proc 2002 ACM Symp Principles of Database Systems (PODS’02). Madison, WI. June 2002;1–16.

51. Beckman RJ, Cook RD. Outlier…s. Technometrics. 1983;25:119–149.

52. Buettcher S, Clarke CLA, Cormack GV. Information Retrieval: Implementing and Evaluating Search Engines Cambridge, MA: MIT Press; 2010.

53. Burdick D, Calimlim M, Gehrke. J. MAFIA: A maximal frequent itemset algorithm for transactional databases. In: Proc 2001 Int Conf Data Engineering (ICDE’01). Heidelberg, Germany. Apr. 2001;443–452.

54. Brown DE, Corruble V, Pittard CL. A comparison of decision tree classifiers with backpropagation neural networks for multimodal classification problems. Pattern Recognition. 1993;26:953–961.

55. Bickel PJ, Doksum KA. Mathematical Statistics: Basic Ideas and Selected Topics Prentice-Hall 2001; Vol. 1.

56. Brockwell PJ, Davis RA. Introduction to Time Series and Forecasting 2nd ed. New York: Springer; 2002.

57. Barbará D, DuMouchel W, Faloutsos C, et al. The New Jersey data reduction report. Bull Technical Committee on Data Engineering. Dec. 1997;20:3–45.

58. Bruce A, Donoho D, Gao H-Y. Wavelet analysis. IEEE Spectrum. Oct. 1996;33:26–35.

59. Burdick D, Deshpande P, Jayram TS, Ramakrishnan R, Vaithyanathan S. OLAP over uncertain and imprecise data. In: Proc 2005 Int Conf Very Large Data Bases (VLDB’05). Trondheim, Norway. Aug. 2005;970–981.

60. Benninga S. Financial Modeling 3rd. ed. Cambridge, MA: MIT Press; 2008.

61. Bertin J. Graphics and Graphic Information Processing Berlin: Walter de Gruyter; 1981.

62. Berry MW. Survey of Text Mining: Clustering, Classification, and Retrieval New York: Springer; 2003.

63. Bezdek JC. Pattern Recognition with Fuzzy Objective Function Algorithms Plenum Press 1981.

64. Breiman L, Friedman J, Olshen R, Stone C. Classification and Regression Trees Wadsworth International Group 1984.

65. Bradley P, Fayyad U, Reina C. Scaling clustering algorithms to large databases. In: Proc 1998 Int Conf Knowledge Discovery and Data Mining (KDD’98). New York. Aug. 1998;9–15.

66. Bhattacharya I, Getoor L. Iterative record linkage for cleaning and integration. In: Proc SIGMOD 2004 Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD’04). Paris, France. June 2004;11–18.

67. Ben-Gal I. Outlier detection. In: Maimon O, Rockach L, eds. Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers. Kluwer Academic. 2005.

68. Bucila C, Gehrke J, Kifer D, White W. DualMiner: A dual-pruning algorithm for itemsets with constraints. Data Mining and Knowledge Discovery. 2003;7:241–272.

69. Bonchi F, Giannotti F, Mazzanti A, Pedreschi D. ExAnte: Anticipated data reduction in constrained pattern mining. In: Cavtat-Dubrovnik, Croatia Sept. 2003;59–70. Proc 7th European Conf Principles and Pratice of Knowledge Discovery in Databases (PKDD’03). Vol. 2838/2003.

70. Beyer KS, Goldstein J, Ramakrishnan R, Shaft U. When is “nearest neighbor” meaningful? In: Proc 1999 Int Conf Database Theory (ICDT’99). Jerusalem, Israel. Jan. 1999;217–235.

71. Boser B, Guyon I, Vapnik VN. A training algorithm for optimal margin classifiers. In: Proc Fifth Annual Workshop on Computational Learning Theory. San Mateo, CA: ACM Press; 1992;144–152.

72. Bishop CM. Neural Networks for Pattern Recognition Oxford University Press 1995.

73. Bishop CM. Pattern Recognition and Machine Learning New York: Springer; 2006.

74. Box GEP, Jenkins GM, Reinsel GC. Time Series Analysis: Forecasting and Control 4th ed. Prentice-Hall 2008.

75. Breunig MM, Kriegel H-P, Ng R, Sander J. LOF: Identifying density-based local outliers. In: Proc 2000 ACM-SIGMOD Int Conf Management of Data (SIGMOD’00). Dallas, TX. May 2000;93–104.

76. Berry MJA, Linoff G. Mastering Data Mining: The Art and Science of Customer Relationship Management John Wiley & Sons 1999.

77. Berry MJA, Linoff GS. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management John Wiley & Sons 2004.

78. Blei D, Lafferty J. Topic models. In: Srivastava A, Sahami M, eds. Text Mining: Theory and Applications. Taylor and Francis. 2009.

79. Barbará D, Li Y, Couto J, Lin J-L, Jajodia S. Bootstrapping a data mining intrusion detection system. In: Proc 2003 ACM Symp on Applied Computing (SAC’03). Melbourne, FL. March 2003.

80. Blum A, Mitchell T. Combining labeled and unlabeled data with co-training. In: Proc 11th Conf Computational Learning Theory (COLT’98). Madison, WI. 1998;92–100.

81. Bakar ZA, Mohemad R, Ahmad A, Deris MM. A comparative study for outlier detection techniques in data mining. In: Proc 2006 IEEE Conf Cybernetics and Intelligent Systems. Bangkok, Thailand. 2006;1–6.

82. Brin S, Motwani R, Silverstein C. Beyond market basket: Generalizing association rules to correlations. In: Proc 1997 ACM-SIGMOD Int Conf Management of Data (SIGMOD’97). Tucson, AZ. May 1997;265–276.

83. Brin S, Motwani R, Ullman JD, Tsur S. Dynamic itemset counting and implication rules for market basket analysis. In: Proc 1997 ACM-SIGMOD Int Conf Management of Data (SIGMOD’97). Tucson, AZ. May 1997;255–264.

84. Buntine WL, Niblett T. A further comparison of splitting rules for decision-tree induction. Machine Learning. 1992;8:75–85.

85. Baxevanis A, Ouellette BFF. Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins 3rd ed. John Wiley & Sons 2004.

86. Bezdek JC, Pal SK. Fuzzy Models for Pattern Recognition: Methods That Search for Structures in Data IEEE Press 1992.

87. Brin S, Page L. The anatomy of a large-scale hypertextual web search engine. In: Proc 7th Int World Wide Web Conf (WWW’98). Brisbane, Australia. Apr. 1998;107–117.

88. Baralis E, Paraboschi S, Teniente E. Materialized view selection in a multidimensional database. In: Proc 1997 Int Conf Very Large Data Bases (VLDB’97). Athens, Greece. Aug. 1997; pp. 98–12.

89. Bareiss ER, Porter BW, Weir CC. Protos: An exemplar-based learning apprentice. Int J Man-Machine Studies. 1988;29:549–561.

90. Beyer K, Ramakrishnan R. Bottom-up computation of sparse and iceberg cubes. In: Proc 1999 ACM-SIGMOD Int Conf Management of Data (SIGMOD’99). Philadelphia, PA. June 1999;359–370.

91. Breiman L. Bagging predictors. Machine Learning. 1996;24:123–140.

92. Breiman L. Random forests. Machine Learning. 2001;45:5–32.

93. Barbará D, Sullivan M. Quasi-cubes: Exploiting approximation in multidimensional databases. SIGMOD Record. 1997;26:12–17.

94. Bay SD, Schwabacher M. Mining distance-based outliers in near linear time with randomization and a simple pruning rule. In: Proc 2003 ACM SIGKDD Int Conf Knowledge Discovery and Data Mining (KDD’03). Washington, DC. Aug. 2003;29–38.

95. Berson A, Smith SJ, Thearling K. Building Data Mining Applications for CRM McGraw-Hill 1999.

96. Ballou DP, Tayi GK. Enhancing data quality in data warehouse environments. Communications of the ACM. 1999;42:73–78.

97. Brodley CE, Utgoff PE. Multivariate decision trees. Machine Learning. 1995;19:45–77.

98. Buntine WL. Operations for learning with graphical models. J Artificial Intelligence Research. 1994;2:159–225.

99. Burges CJC. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery. 1998;2:121–168.

100. Barbará D, Wu X. Using loglinear models to compress datacubes. In: Proc 1st Int Conf Web-Age Information Management (WAIM’00). Shanghai, China. 2000;311–322.

101. Babu S, Widom J. Continuous queries over data streams. SIGMOD Record. 2001;30:109–120.

102. Baeza-Yates RA, Ribeiro-Neto BA. Modern Information Retrieval 2nd ed. Boston: Addison-Wesley; 2011.

103. J. Catlett. Megainduction: Machine Learning on Very large Databases. Ph.D. Thesis, University of Sydney, 1991.

104. Chandola V, Banerjee A, Kumar V. Anomaly detection: A survey. ACM Computing Surveys. 2009;41:1–58.

105. Cheng Y, Church G. Biclustering of expression data. In: Proc 2000 Int Conf Intelligent Systems for Molecular Biology (ISMB’00). La Jolla, CA. Aug. 2000;93–103.

106. Cai Y, Cercone N, Han J. Attribute-oriented induction in relational databases. In: Piatetsky-Shapiro G, Frawley WJ, eds. Knowledge Discovery in Databases. AAAI/MIT Press. 1991;213–228.

107. Chen B-C, Chen L, Lin Y, Ramakrishnan R. Prediction cubes. In: Proc 2005 Int Conf Very Large Data Bases (VLDB’05). Trondheim, Norway. Aug. 2005;982–993.

108. Codd EF, Codd SB, Salley CT. Beyond decision support. Computer World. July 1993;27(30):5–12.

109. Chaudhuri S, Dayal U. An overview of data warehousing and OLAP technology. SIGMOD Record. 1997;26:65–74.

110. Chen Y, Dong G, Han J, Wah BW, Wang J. Multidimensional regression analysis of time-series data streams. In: Proc 2002 Int Conf Very Large Data Bases (VLDB’02). Hong Kong, China. Aug. 2002;323–334.

111. Chen Y, Dong G, Han J, Pei J, Wah BW, Wang J. Regression cubes with lossless compression and aggregation. IEEE Trans Knowledge and Data Engineering. 2006;18:1585–1599.

112. Chakrabarti S, Dom BE, Indyk P. Enhanced hypertext classification using hyper-links. In: Proc 1998 ACM-SIGMOD Int Conf Management of Data (SIGMOD’98). Seattle, WA. June 1998;307–318.

113. Chakrabarti S, Dom BE, Kumar SR, et al. Mining the web’s link structure. COMPUTER. 1999;32:60–67.

114. Chaturvedi A, Green P, Carroll J. k-means, k-medians and k-modes: Special cases of partitioning multiway data. In: The Classification Society of North America (CSNA) Meeting Presentation. Houston, TX. 1994.

115. Chaturvedi A, Green P, Carroll J. k-modes clustering. J Classification. 2001;18:35–55.

116. Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Information Theory. 1967;13:21–27.

117. Cooper G, Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Machine Learning. 1992;9:309–347.

118. Cook DJ, Holder LB. Mining Graph Data John Wiley & Sons 2007.

119. Chakrabarti S. Mining the Web: Discovering Knowledge from Hypertext Data Morgan Kaufmann 2003.

120. Chatfield C. The Analysis of Time Series: An Introduction 6th ed. Chapman & Hall 2003.

121. Cheung DW, Han J, Ng V, Fu A, Fu Y. A fast distributed algorithm for mining association rules. In: Proc 1996 Int Conf Parallel and Distributed Information Systems. Miami Beach, FL. Dec. 1996;31–44.

122. Cheung DW, Han J, Ng V, Wong CY. Maintenance of discovered association rules in large databases: An incremental updating technique. In: Proc 1996 Int Conf Data Engineering (ICDE’96). New Orleans, LA. Feb. 1996;106–114.

123. Chen MS, Han J, Yu. PS. Data mining: An overview from a database perspective. IEEE Trans Knowledge and Data Engineering. 1996;8:866–883.

124. Carey M, Kossman D. Reducing the braking distance of an SQL query engine. In: Proc 1998 Int Conf Very Large Data Bases (VLDB’98). New York. Aug. 1998;158–169.

125. Chakrabarti D, Kumar R, Tomkins A. Evolutionary clustering. In: Proc 2006 ACM SIGKDD Int Conf Knowledge Discovery in Databases (KDD’06). Philadelphia, PA. Aug. 2006;554–560.

126. Cleveland W. Visualizing Data Hobart Press 1993.

127. Chapelle O, Schölkopf B, Zien A. Semi-supervised Learning Cambridge, MA: MIT Press; 2006.

128. Curram SP, Mingers J. Neural networks, decision tree induction and discriminant analysis: An empirical comparison. J Operational Research Society. 1994;45:440–450.

129. Cao H, Mamoulis N, Cheung DW. Mining frequent spatio-temporal sequential patterns. In: Proc 2005 Int Conf Data Mining (ICDM’05). Houston, TX. Nov. 2005;82–89.

130. Croft B, Metzler D, Strohman T. Search Engines: Information Retrieval in Practice Boston: Addison-Wesley; 2009.

131. Clark P, Niblett T. The CN2 induction algorithm. Machine Learning. 1989;3:261–283.

132. Cohen W. Fast effective rule induction. In: Proc 1995 Int Conf Machine Learning (ICML’95). Tahoe City, CA. July 1995;115–123.

133. Cooper GF. The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence. 1990;42:393–405.

134. Cios K, Pedrycz W, Swiniarski R. Data Mining Methods for Knowledge Discovery Kluwer Academic 1998.

135. Chauvin Y, Rumelhart D. Backpropagation: Theory, Architectures, and Applications Lawrence Erlbaum 1995.

136. Crawford SL. Extensions to the CART algorithm. Int J Man-Machine Studies. Aug. 1989;31:197–217.

137. Chen B-C, Ramakrishnan R, Shavlik JW, Tamma P. Bellwether analysis: Predicting global aggregates from local regions. In: Proc 2006 Int Conf Very Large Data Bases (VLDB’06). Seoul, Korea. Sept. 2006;655–666.

138. Chan PK, Stolfo SJ. Experiments on multistrategy learning by metalearning. In: Proc 2nd Int Conf Information and Knowledge Management (CIKM’93). Washington, DC. Nov. 1993;314–323.

139. Chan PK, Stolfo SJ. Toward multi-strategy parallel & distributed learning in sequence analysis. In: Proc 1st Int Conf Intelligent Systems for Molecular Biology (ISMB’93). Bethesda, MD. July 1993;65–73.

140. Craven MW, Shavlik JW. Extracting tree-structured representations of trained networks. In: Touretzky D, Mozer M, Hasselmo M, eds. Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press; 1996.

141. Craven MW, Shavlik JW. Using neural networks in data mining. Future Generation Computer Systems. 1997;13:211–229.

142. Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods Cambridge University Press 2000.

143. Chi Y, Song X, Zhou D, Hino K, Tseng BL. Evolutionary spectral clustering by incorporating temporal smoothness. In: Proc 2007 ACM SIGKDD Intl Conf Knowledge Discovery and Data Mining (KDD’07). San Jose, CA. Aug. 2007;153–162.

144. Cong G, Tan K-Lee, Tung AKH, Xu X. Mining top-k covering rule groups for gene expression data. In: Proc 2005 ACM-SIGMOD Int Conf Management of Data (SIGMOD’05). Baltimore, MD. June 2005;670–681.

145. Cong G, Wang L, Lin C-Y, Song Y-I, Sun Y. Finding question-answer pairs from online forums. In: Proc 2008 Int ACM SIGIR Conf Research and Development in Information Retrieval (SIGIR’08). Singapore. July 2008;467–474.

146. Cheng H, Yan X, Han J, Hsu C-W. Discriminative frequent pattern analysis for effective classification. In: Proc 2007 Int Conf Data Engineering (ICDE’07). Istanbul, Turkey. Apr. 2007;716–725.

147. Cheng H, Yan X, Han J, Yu PS. Direct discriminative pattern mining for effective classification. In: Proc 2008 Int Conf Data Engineering (ICDE’08). Cancun, Mexico. Apr. 2008;169–178.

148. Chen C, Yan X, Zhu F, Han J, Yu PS. Graph OLAP: Towards online analytical processing on graphs. In: Proc 2008 Int Conf Data Mining (ICDM’08). Pisa, Italy. Dec. 2008;103–112.

149. Darwiche A. Bayesian networks. Communications of the ACM. 2010;53:80–90.

150. Dasarathy BV. Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques IEEE Computer Society Press 1991.

151. Daubechies I. Ten Lectures on Wavelets Capital City Press 1992.

152. Dietterich TG, Bakiri G. Solving multiclass learning problems via error-correcting output codes. J Artificial Intelligence Research. 1995;2:263–286.

153. Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik VN. Support vector regression machines. In: Mozer M, Jordan M, Petsche T, eds. Advances in Neural Information Processing Systems 9. Cambridge, MA: MIT Press; 1997;155–161.

154. Day WHE, Edelsbrunner H. Efficient algorithms for agglomerative hierarchical clustering methods. J Classification. 1984;1:7–24.

155. Dzeroski S, Lavrac N, eds. Relational Data Mining. New York: Springer; 2001.

156. Durbin R, Eddy S, Krogh A, Mitchison G. Biological Sequence Analysis: Probability Models of Proteins and Nucleic Acids Cambridge University Press 1998.

157. Devore JL. Probability and Statistics for Engineering and the Sciences 4th ed. Duxbury Press 1995.

158. Devore JL. Probability and Statistics for Engineering and the Sciences 6th ed. Duxbury Press 2003.

159. Donath WE, Hoffman AJ. Lower bounds for the partitioning of graphs. IBM J Research and Development. 1973;17:420–425.

160. Domingos P, Hulten G. Mining high-speed data streams. In: Proc 2000 ACM SIGKDD Int Conf Knowledge Discovery in Databases (KDD’00). Boston, MA. Aug. 2000;71–80.

161. Dong G, Han J, Lam J, Pei J, Wang K. Mining multi-dimensional constrained gradients in data cubes. In: Proc 2001 Int Conf Very Large Data Bases (VLDB’01). Rome, Italy. Sept. 2001;321–330.

162. Dong G, Han J, Lam J, Pei J, Wang K, Zou W. Mining constrained gradients in multi-dimensional databases. IEEE Trans Knowledge and Data Engineering. 2004;16:922–938.

163. Duda RO, Hart PE, Stork DG. Pattern Classification 2nd ed. John Wiley & Sons 2001.

164. Dasu T, Johnson T. Exploratory Data Mining and Data Cleaning John Wiley & Sons 2003.

165. Dasu T, Johnson T, Muthukrishnan S, Shkapenyuk V. Mining database structure; or how to build a data quality browser. In: Proc 2002 ACM-SIGMOD Int Conf Management of Data (SIGMOD’02). Madison, WI. June 2002;240–251.

166. Dash M, Liu H. Feature selection methods for classification. Intelligent Data Analysis. 1997;1:131–156.

167. Dong G, Li J. Efficient mining of emerging patterns: Discovering trends and differences. In: Proc 1999 Int Conf Knowledge Discovery and Data Mining (KDD’99). San Diego, CA. Aug. 1999;43–52.

168. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J Royal Statistical Society, Series B. 1977;39:1–38.

169. Dash M, Liu H, Yao J. Dimensionality reduction of unsupervised data. In: Proc 1997 IEEE Int Conf Tools with AI (ICTAI’97). Newport Beach, CA: IEEE Computer Society; 1997;532–539.

170. Dasgupta D, Majumdar NS. Anomaly detection in multidimensional data using negative selection algorithm. In: Proc 2002 Congress on Evolutionary Computation (CEC’02). Washington, DC. 2002;1039–1044. Chapter 12.

171. Deshpande P, Naughton J, Ramasamy K, Shukla A, Tufte K, Zhao Y. Cubing algorithms, storage estimation, and storage and processing alternatives for OLAP. Bull Technical Committee on Data Engineering. 1997;20:3–11.

172. Dobson AJ. An Introduction to Generalized Linear Models Chapman & Hall 1990.

173. Dobson AJ. An Introduction to Generalized Linear Models 2nd ed. Chapman & Hall 2001.

174. Domingos P. The RISE system: Conquering without separating. In: Proc 1994 IEEE Int Conf Tools with Artificial Intelligence (TAI’94). New Orleans, LA. 1994;704–707.

175. Domingos P. The role of Occam’s razor in knowledge discovery. Data Mining and Knowledge Discovery. 1999;3:409–425.

176. Domingos P, Pazzani M. Beyond independence: Conditions for the optimality of the simple Bayesian classifier. In: Proc 1996 Int Conf Machine Learning (ML’96). Bari, Italy. July 1996;105–112.

177. Devore J, Peck R. Statistics: The Exploration and Analysis of Data Duxbury Press 1997.

178. Dong G, Pei J. Sequence Data Mining New York: Springer; 2007.

179. Donjerkovic D, Ramakrishnan R. Probabilistic optimization of top N queries. In: Proc 1999 Int Conf Very Large Data Bases (VLDB’99). Edinburgh, UK. Sept. 1999;411–422.

180. Davidson I, Ravi SS. Clustering with constraints: Feasibility issues and the k-means algorithm. In: Proc 2005 SIAM Int Conf Data Mining (SDM’05). Newport Beach, CA. Apr. 2005.

181. Dhar V, Tuzhilin A. Abstract-driven pattern discovery in databases. IEEE Trans Knowledge and Data Engineering. 1993;5:926–938.

182. Dunham M. Data Mining: Introductory and Advanced Topics Prentice-Hall 2003.

183. Davidson I, Wagstaff KL, Basu S. Measuring constraint-set utility for partitional clustering algorithms. In: Proc 10th European Conf Principles and Practice of Knowledge Discovery in Databases (PKDD’06). Berlin, Germany. Sept. 2006;115–126.

184. Dwork C. Differential privacy. In: Proc 2006 Int Col Automata, Languages and Programming (ICALP). Venice, Italy. July 2006;1–12.

185. Dai W, Yang Q, Xue G, Yu Y. Boosting for transfer learning. In: Proc 24th Intl Conf Machine Learning. Corvallis, OR. June 2007;193–200.

186. Egan JP. Signal Detection Theory and ROC Analysis Academic Press 1975.

187. Easley D, Kleinberg J. Networks, Crowds, and Markets: Reasoning about a Highly Connected World Cambridge University Press 2010.

188. Eskin E. Anomaly detection over noisy data using learned probability distributions. In: Proc 17th Int Conf Machine Learning (ICML’00). Stanford, CA. 2000.

189. Ester M, Kriegel H-P, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases. In: Proc 1996 Int Conf Knowledge Discovery and Data Mining (KDD’96). Portland, OR. Aug. 1996;226–231.

190. Ester M, Kriegel H-P, Xu X. Knowledge discovery in large spatial databases: Focusing techniques for efficient class identification. In: Proc 1995 Int Symp Large Spatial Databases (SSD’95). Portland, ME. Aug. 1995;67–82.

191. Elkan C. Boosting and naïve Bayesian learning. In: Technical Report CS97-557. University of California at San Diego: Dept. Computer Science and Engineering; Sept. 1997.

192. Elkan C. The foundations of cost-sensitive learning. In: Proc 17th Intl Joint Conf Artificial Intelligence (IJCAI’01). Seattle, WA. 2001;973–978.

193. Elmasri R, Navathe SB. Fundamentals of Database Systems 6th ed. Boston: Addison-Wesley; 2010.

194. English L. Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits John Wiley & Sons 1999.

195. Evfimievski A, Srikant R, Agrawal R, Gehrke J. Privacy preserving mining of association rules. In: Proc 2002 ACM SIGKDD Int Conf Knowledge Discovery and Data Mining (KDD’02). Edmonton, Alberta, Canada. July 2002;217–228.

196. Efron B, Tibshirani R. An Introduction to the Bootstrap Chapman & Hall 1993.

197. Finkel RA, Bentley JL. Quad-trees: A data structure for retrieval on composite keys. ACTA Informatica. 1974;4:1–9.

198. Friedman J, Bogdan EP. Predictive learning via rule ensembles. Ann Applied Statistics. 2008;2:916–954.

199. Friedman JH, Bentley JL, Finkel RA. An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Math Software. 1977;3:209–226.

200. Faloutsos M, Faloutsos P, Faloutsos C. On power-law relationships of the internet topology. In: Proc ACM SIGCOMM’99 Conf Applications, Technologies, Architectures, and Protocols for Computer Communication. Cambridge, MA. Aug. 1999;251–262.

201. Fishelson M, Geiger D. Exact genetic linkage computations for general pedigrees. Disinformation. 2002;18:189–198.

202. Fagin R, Guha RV, Kumar R, Novak J, Sivakumar D, Tomkins A. Multi-structural databases. In: Proc 2005 ACM SIGMOD-SIGACT-SIGART Symp Principles of Database Systems (PODS’05). Baltimore, MD. June 2005;184–195.

203. Fayyad U, Grinstein G, Wierse A. Information Visualization in Data Mining and Knowledge Discovery Morgan Kaufmann 2001.

204. Fix E, Hodges Jr JL. Discriminatory analysis, non-parametric discrimination: Consistency properties. In: Technical Report 21-49-004(4). Randolph Field, Texas: USAF School of Aviation Medicine; 1951.

205. Fukunaga K, Hummels D. Bayes error estimation using Parzen and k-nn procedure. IEEE Trans Pattern Analysis and Machine Learning. 1987;9:634–643.

206. Fu Y, Han J. Meta-rule-guided mining of association rules in relational databases. In: Proc 1995 Int Workshop Integration of Knowledge Discovery with Deductive and Object-Oriented Databases (KDOOD’95). Singapore. Dec. 1995;39–46.

207. Fayyad UM, Irani KB. What should be minimized in a decision tree? In: Proc 1990 Nat Conf Artificial Intelligence (AAAI’90). Boston, MA. 1990;749–754.

208. Fayyad UM, Irani KB. The attribute selection problem in decision tree generation. In: Proc 1992 Nat Conf Artificial Intelligence (AAAI’92). San Jose, CA. 1992;104–110.

209. Fayyad U, Irani K. Multi-interval discretization of continuous-valued attributes for classification learning. In: Proc 1993 Int Joint Conf Artificial Intelligence (IJCAI’93). Chambery, France. 1993;1022–1029.

210. Fiedler M. Algebraic connectivity of graphs. Czechoslovak Mathematical J. 1973;23:298–305.

211. Fahlman S, Lebiere C. The cascade-correlation learning algorithm. In: Technical Report CMU-CS-90-100. Computer Sciences Department, Carnegie Mellon University. 1990.

212. Faloutsos C, Lin K-I. FastMap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. In: Proc 1995 ACM-SIGMOD Int Conf Management of Data (SIGMOD’95). San Jose, CA. May 1995;163–174.

213. Fletcher R. Practical Methods of Optimization John Wiley & Sons 1987.

214. Fukuda T, Morimoto Y, Morishita S, Tokuyama T. Data mining using two-dimensional optimized association rules: Scheme, algorithms, and visualization. In: Proc 1996 ACM-SIGMOD Int Conf Management of Data (SIGMOD’96). Montreal, Quebec, Canada. June 1996;13–23.

215. J. Friedman and B. E. Popescu. Predictive learning via rule ensembles. In Technical Report, Department of Statistics, Stanford University, 2005.

216. Freedman D, Pisani R, Purves R. Statistics 4th ed. W. W. Norton & Co. 2007.

217. Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R, eds. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press 1996.

218. Fawcett T, Provost F. Adaptive fraud detection. Data Mining and Knowledge Discovery. 1997;1:291–316.

219. Fraley C, Raftery AE. Model-based clustering, discriminant analysis, and density estimation. J American Statistical Association. 2002;97:611–631.

220. Friedman JH. A recursive partitioning decision rule for nonparametric classifiers. IEEE Trans Computer. 1977;26:404–408.

221. Friedman JH. Greedy function approximation: A gradient boosting machine. Ann Statistics. 2001;29:1189–1232.

222. N. Friedman. Pcluster: Probabilistic agglomerative clustering of gene expression profiles. In Technical Report 2003-80, Hebrew University, 2003.

223. Faloutsos C, Ranganathan M, Manolopoulos Y. Fast subsequence matching in time-series databases. In: Proc 1994 ACM-SIGMOD Int Conf Management of Data (SIGMOD’94. Minneapolis, MN. May 1994;419–429.

224. Fayyad U, Smyth P. Image database exploration: Progress and challenges. In: Proc AAAI’93 Workshop Knowledge Discovery in Databases (KDD’93). Washington, DC. July 1993;14–27.

225. Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. J Computer and System Sciences. 1997;55:119–139.

226. Feldman R, Sanger J. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data Cambridge University Press 2006.

227. Fang M, Shivakumar N, Garcia-Molina H, Motwani R, Ullman JD. Computing iceberg queries efficiently. In: Proc 1998 Int Conf Very Large Data Bases (VLDB’98). New York, NY. Aug. 1998;299–310.

228. Furnkranz J, Widmer G. Incremental reduced error pruning. In: Proc 1994 Int Conf Machine Learning (ICML’94). New Brunswick, NJ. 1994;70–77.

229. Fung BCM, Wang K, Fu AW-C, Yu PS. Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques Chapman & Hall/CRC 2010.

230. Fujimaki R, Yairi T, Machida K. An approach to spacecraft anomaly detection problem using kernel feature space. In: Proc 2005 Int Workshop Link Discovery (LinkKDD’05). Chicago, IL. 2005;401–410.

231. Gallant SI. Neural Network Learning and Expert Systems Cambridge, MA: MIT Press; 1993.

232. Gates B. Business @ the Speed of Thought: Succeeding in the Digital Economy Warner Books 2000.

233. Gray J, Chaudhuri S, Bosworth A, et al. Data cube: A relational aggregation operator generalizing group-by, cross-tab and sub-totals. Data Mining and Knowledge Discovery. 1997;1:29–54.

234. Getoor L, Friedman N, Koller D, Taskar B. Learning probabilistic models of relational structure. In: Proc 2001 Int Conf Machine Learning (ICML’01). Williamstown, MA. 2001;170–177.

235. Galhardas H, Florescu D, Shasha D, Simon E, Saita C-A. Declarative data cleaning: Language, model, and algorithms. In: Proc 2001 Int Conf Very Large Data Bases (VLDB’01). Rome, Italy. Sept. 2001;371–380.

236. Gersho A, Gray RM. Vector Quantization and Signal Compression Kluwer Academic 1992.

237. Gaede V, Günther O. Multidimensional access methods. ACM Computing Surveys. 1998;30:170–231.

238. Ganti V, Gehrke JE, Ramakrishnan R. CACTUS—clustering categorical data using summaries. In: Proc 1999 Int Conf Knowledge Discovery and Data Mining (KDD’99). San Diego, CA. 1999;73–83.

239. Gehrke J, Ganti V, Ramakrishnan R, Loh W-Y. BOAT—optimistic decision tree construction. In: Proc 1999 ACM-SIGMOD Int Conf Management of Data (SIGMOD’99). Philadelphia, PA. June 1999;169–180.

240. Gonzalez H, Han J, Li X. Flowcube: Constructuing RFID flowcubes for multi-dimensional analysis of commodity flows. In: Proc 2006 Int Conf Very Large Data Bases (VLDB’06). Seoul, Korea. Sept. 2006;834–845.

241. Gonzalez H, Han J, Li X, Klabjan D. Warehousing and analysis of massive RFID data sets. In: Proc 2006 Int Conf Data Engineering (ICDE’06). Atlanta, GA. Apr. 2006;83.

242. Grossman RL, Kamath C, Kegelmeyer P, Kumar V, Namburu RR. Data Mining for Scientific and Engineering Applications Kluwer Academic 2001.

243. Gibson D, Kleinberg JM, Raghavan P. Clustering categorical data: An approach based on dynamical systems. In: Proc 1998 Int Conf Very Large Data Bases (VLDB’98). New York, NY. Aug. 1998;311–323.

244. Gupta A, Mumick IS. Materialized Views: Techniques, Implementations, and Applications Cambridge, MA: MIT Press; 1999.

245. Guha S, Mishra N, Motwani R, O’Callaghan L. Clustering data streams. In: Proc 2000 Symp Foundations of Computer Science (FOCS’00). Redondo Beach, CA. 2000;359–366.

246. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. Feb. 2009;457:1012–1014.

247. Garcia-Molina H, Ullman JD, Widom J. Database Systems: The Complete Book 2nd ed. Prentice Hall 2008.

248. Guyon I, Matic N, Vapnik V. Discoverying informative patterns and data cleaning. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R, eds. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press. 1996;181–203.

249. Goldberg D. Genetic Algorithms in Search, Optimization, and Machine Learning Reading, MA: Addison-Wesley; 1989.

250. Grossman DA, Frieder O. Information Retrieval: Algorithms and Heuristics New York: Springer; 2004.

251. Grunwald PD, Rissanen J. The Minimum Description Length Principle Cambridge, MA: MIT Press; 2007.

252. Gehrke J, Ramakrishnan R, Ganti V. RainForest: A framework for fast decision tree construction of large datasets. In: Proc 1998 Int Conf Very Large Data Bases (VLDB’98). New York, NY. Aug. 1998;416–427.

253. Guha S, Rastogi R, Shim K. CURE: An efficient clustering algorithm for large databases. In: Proc 1998 ACM-SIGMOD Int Conf Management of Data (SIGMOD’98). Seattle, WA. June 1998;73–84.

254. Guha S, Rastogi R, Shim K. ROCK: A robust clustering algorithm for categorical attributes. In: Proc 1999 Int Conf Data Engineering (ICDE’99). Sydney, Australia. Mar. 1999;512–521.

255. Grubbs FE. Procedures for detecting outlying observations in samples. Technometrics. 1969;11:1–21.

256. Gupta H. Selection of views to materialize in a data warehouse. In: Proc 7th Int Conf Database Theory (ICDT’97). Delphi, Greece. Jan. 1997;98–112.

257. Guttman A. R-Tree: A dynamic index structure for spatial searching. In: Proc 1984 ACM-SIGMOD Int Conf Management of Data (SIGMOD’84). Boston, MA. June 1984;47–57.

258. Gonzalez RC, Woods RE. Digital Image Processing 3rd ed. Prentice Hall 2007.

259. Goethals B, Zaki M. An introduction to workshop frequent itemset mining implementations. In: Proc ICDM’03 Int Workshop Frequent Itemset Mining Implementations (FIMI’03). Melbourne, FL. Nov. 2003;1–13.

260. Grahne G, Zhu J. Efficiently using prefix-trees in mining frequent itemsets. In: Proc ICDM’03 Int Workshop on Frequent Itemset Mining Implementations (FIMI’03). Melbourne, FL. Nov. 2003.

261. Hodge VJ, Austin J. A survey of outlier detection methodologies. Artificial Intelligence Review. 2004;22:85–126.

262. Hellerstein JM, Avnur R, Chou A, et al. Interactive data analysis: The control project. IEEE Computer. 1999;32:51–59.

263. Hamilton J. Time Series Analysis Princeton University Press 1994.

264. Han J. Towards on-line analytical mining in large databases. SIGMOD Record. 1998;27:97–107.

265. Hart PE. The condensed nearest neighbor rule. IEEE Trans Information Theory. 1968;14:515–516.

266. Hartigan J. Direct clustering of a data matrix. J American Stat Assoc. 1972;67:123–129.

267. Hartigan JA. Clustering Algorithms John Wiley & Sons 1975.

268. Hawkins DM. Identification of Outliers Chapman & Hall 1980.

269. Haykin SS. Neural Networks: A Comprehensive Foundation Prentice-Hall 1999.

270. Haykin S. Neural Networks and Learning Machines Prentice-Hall 2008.

271. Hanson SJ, Burr DJ. Minkowski-r back-propagation: Learning in connectionist models with non-euclidian error signals. In: Neural Information Proc Systems Conf. Denver, CO. 1987;348–357.

272. Halkidi M, Batistakis Y, Vazirgiannis M. On clustering validation techniques. J Intelligent Information Systems. 2001;17:107–145.

273. Han J, Cai Y, Cercone N. Data-driven discovery of quantitative rules in relational databases. IEEE Trans Knowledge and Data Engineering. 1993;5:29–40.

274. Holder LB, Cook DJ, Djoko S. Substructure discovery in the subdue system. In: Proc AAAI’94 Workshop on Knowledge Discovery in Databases (KDD’94). Seattle, WA. July 1994;169–180.

275. Heckerman D. Bayesian networks for knowledge discovery. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R, eds. Advances in Knowledge Discovery and Data Mining. Cambridge, MA: MIT Press; 1996;273–305.

276. Han J, Fu Y. Dynamic generation and refinement of concept hierarchies for knowledge discovery in databases. In: Proc AAAI’94 Workshop Knowledge Discovery in Databases (KDD’94). Seattle, WA. July 1994;157–168.

277. Han J, Fu Y. Discovery of multiple-level association rules from large databases. In: Proc 1995 Int Conf Very Large Data Bases (VLDB’95). Zurich, Switzerland. Sept. 1995;420–431.

278. Han J, Fu Y. Exploration of the power of attribute-oriented induction in data mining. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R, eds. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press. 1996;399–421.

279. Horn PS, Feng L, Li Y, Pesce AJ. Effect of outliers and nonhealthy individuals on reference interval estimation. Clinical Chemistry. 2001;47:2137–2145.

280. Heller KA, Ghahramani Z. Bayesian hierarchical clustering. In: Proc 22nd Int Conf Machine Learning (ICML’05). Bonn, Germany. 2005;297–304.

281. Hinneburg A, Gabriel H-H. DENCLUE 2.0: Fast clustering based on kernel density estimation. In: Proc 2007 Int Conf Intelligent Data Analysis (IDA’07). Ljubljana, Slovenia. 2007;70–80.

282. Heckerman D, Geiger D, Chickering DM. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning. 1995;20:197–243.

283. Hilderman RJ, Hamilton HJ. Knowledge Discovery and Measures of Interest Kluwer Academic 2001.

284. Hellerstein J, Haas P, Wang H. Online aggregation. In: Proc 1997 ACM-SIGMOD Int Conf Management of Data (SIGMOD’97). Tucson, AZ. May 1997;171–182.

285. Higgins RC. Analysis for Financial Management with S&P Bind-In Card Irwin/McGraw-Hill 2008.

286. Hoschka P, Klösgen W. A support system for interpreting statistical data. In: Piatetsky-Shapiro G, Frawley WJ, eds. Knowledge Discovery in Databases. AAAI/MIT Press. 1991;325–346.

287. Hinneburg A, Keim DA. An efficient approach to clustering in large multimedia databases with noise. In: Proc 1998 Int Conf Knowledge Discovery and Data Mining (KDD’98). New York, NY. Aug. 1998;58–65.

288. Hadjieleftheriou M, Kollios G, Gunopulos D, Tsotras VJ. Online discovery of dense areas in spatio-temporal databases. In: Proc 2003 Int Symp Spatial and Temporal Databases (SSTD’03). Santorini Island, Greece. July 2003;306–324.

289. Höppner F, Klawonn F, Kruse R, Runkler T. Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition Wiley 1999.

290. Hertz J, Krogh A, Palmer RG. Introduction to the Theory of Neural Computation Reading, MA: Addison-Wesley; 1991.

291. Hsu W, Lee ML, Wang J. Temporal and Spatio-Temporal Data Mining IGI Publishing 2007.

292. Hsu W, Lee ML, Zhang J. Image mining: Trends and developments. J Intelligent Information Systems. 2002;19:7–23.

293. Hong J, Mozetic I, Michalski RS. Incremental learning of attribute-based descriptions from examples, the method and user’s guide. In: Report ISG 85-5, UIUCDCS-F-86-949. Department of Computer Science, University of Illinois at Urbana-Champaign. 1986.

294. Hunt EB, Marin J, Stone PT. Experiments in Induction Academic Press 1966.

295. Hand DJ, Mannila H, Smyth P. Principles of Data Mining (Adaptive Computation and Machine Learning) Cambridge, MA: MIT Press; 2001.

296. Hecht-Nielsen R. Neurocomputing Reading, MA: Addison-Wesley; 1990.

297. Horak R. Telecommunications and Data Communications Handbook 2nd ed. Wiley-Interscience 2008.

298. Hua M, Pei J. Cleaning disguised missing data: A heuristic approach. In: Proc 2007 ACM SIGKDD Intl Conf Knowledge Discovery and Data Mining (KDD’07). San Jose, CA. Aug. 2007;950–958.

299. Han J, Pei J, Dong G, Wang K. Efficient computation of iceberg cubes with complex measures. In: Proc 2001 ACM-SIGMOD Int Conf Management of Data (SIGMOD’01). Santa Barbara, CA. May 2001;1–12.

300. Hosking J, Pednault E, Sudan M. A statistical perspective on data mining. Future Generation Computer Systems. 1997;13:117–134.

301. Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation. In: Proc 2000 ACM-SIGMOD Int Conf Management of Data (SIGMOD’00). Dallas, TX. May 2000;1–12.

302. Hay M, Rastogi V, Miklau G, Suciu D. Boosting the accuracy of differentially-private queries through consistency. In: Proc 2010 Int Conf Very Large Data Bases (VLDB’10). Singapore. Sept. 2010;1021–1032.

303. Harinarayan V, Rajaraman A, Ullman JD. Implementing data cubes efficiently. In: Proc 1996 ACM-SIGMOD Int Conf Management of Data (SIGMOD’96). Montreal, Quebec, Canada. June 1996;205–216.

304. Hellerstein JM, Stonebraker M. Readings in Database Systems 4th ed. Cambridge, MA: MIT Press; 2005.

305. Harp SA, Samad T, Guha A. Designing application-specific neural networks using the genetic algorithm. In: Touretzky DS, ed. Advances in Neural Information Processing Systems II. Morgan Kaufmann. 1990;447–454.

306. Hastie T, Tibshirani R. Classification by pairwise coupling. Ann Statistics. 1998;26:451–471.

307. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd ed. Springer Verlag 2009.

308. Huang Z. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery. 1998;2:283–304.

309. Huberty CH. Applied Discriminant Analysis Wiley-Interscience 1994.

310. Hubbard BB. The World According to Wavelets A. K. Peters 1996.

311. Huan J, Wang W, Bandyopadhyay D, Snoeyink J, Prins J, Tropsha A. Mining spatial motifs from protein structure graphs. In: Proc 8th Int Conf Research in Computational Molecular Biology (RECOMB). San Diego, CA. Mar. 2004;308–315.

312. He Z, Xu X, Deng S. Discovering cluster-based local outliers. Pattern Recognition Lett. June, 2003;24:1641–1650.

313. Imhoff C, Galemmo N, Geiger JG. Mastering Data Warehouse Design: Relational and Dimensional Techniques John Wiley & Sons 2003.

314. Imielinski T, Khachiyan L, Abdulghani A. Cubegrades: Generalizing association rules. Data Mining and Knowledge Discovery. 2002;6:219–258.

315. Imielinski T, Mannila H. A database perspective on knowledge discovery. Communications of the ACM. 1996;39:58–64.

316. Inmon WH. Building the Data Warehouse John Wiley & Sons 1996.

317. Inokuchi A, Washio T, Motoda H. An apriori-based algorithm for mining frequent substructures from graph data. In: Proc 2000 European Symp Principles of Data Mining and Knowledge Discovery (PKDD’00). Lyon, France. Sept. 1998;13–23.

318. Jacobs R. Increased rates of convergence through learning rate adaptation. Neural Networks. 1988;1:295–307.

319. Jain AK. Data clustering: 50 years beyond k-means. Pattern Recognition Lett. 2010;31(8):651–666.

320. James M. Classification Algorithms John Wiley & Sons 1985.

321. Ji X, Bailey J, Dong G. Mining minimal distinguishing subsequence patterns with gap constraints. In: Proc 2005 Int Conf Data Mining (ICDM’05). Houston, TX. Nov. 2005;194–201.

322. Jain AK, Dubes RC. Algorithms for Clustering Data Prentice-Hall 1988.

323. Jensen FV. An Introduction to Bayesian Networks Springer Verlag 1996.

324. John GH, Langley P. Static versus dynamic sampling for data mining. In: Proc 1996 Int Conf Knowledge Discovery and Data Mining (KDD’96). Portland, OR. Aug. 1996;367–370.

325. Jain AK, Murty MN, Flynn PJ. Data clustering: A survey. ACM Computing Surveys. 1999;31:264–323.

326. G. H. John. Enhancements to the Data Mining Process. Ph.D. Thesis, Computer Science Department, Stanford University, 1997.

327. John GH. Behind-the-scenes data mining: A report on the KDD-98 panel. SIGKDD Explorations. 1999;1:6–8.

328. Jones NC, Pevzner PA. An Introduction to Bioinformatics Algorithms Cambridge, MA: MIT Press; 2004.

329. Ji M, Sun Y, Danilevsky M, Han J, Gao J. Graph regularized transductive classification on heterogeneous information networks. In: Proc 2010 European Conf Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD’10). Barcelona, Spain. Sept. 2010;570–586.

330. Jin W, Tung KH, Han J. Mining top-n local outliers in large databases. In: Proc 2001 ACM SIGKDD Int Conf Knowledge Discovery in Databases (KDD’01). San Fransisco, CA. Aug. 2001;293–298.

331. Jin W, Tung AKH, Han J, Wang W. Ranking outliers using symmetric neighborhood relationship. In: Proc 2006 Pacific-Asia Conf Knowledge Discovery and Data Mining (PAKDD’06). Singapore. Apr. 2006.

332. Johnson RA, Wichern DA. Applied Multivariate Statistical Analysis 3rd ed. Prentice-Hall 1992.

333. Jeh G, Widom J. SimRank: A measure of structural-context similarity. In: Proc 2002 ACM SIGKDD Int Conf Knowledge Discovery and Data Mining (KDD’02). Edmonton, Alberta, Canada. July 2002;538–543.

334. Johnson RA, Wichern DA. Applied Multivariate Statistical Analysis 5th ed. Prentice Hall 2002.

335. Kamath C. Scientific Data Mining: A Practical Perspective Society for Industrial and Applied Mathematic (SIAM) 2009.

336. Kass GV. An exploratory technique for investigating large quantities of categorical data. Applied Statistics. 1980;29:119–127.

337. Kulis B, Basu S, Dhillon I, Mooney R. Semi-supervised graph clustering: A kernel approach. Machine Learning. 2009;74:1–22.

338. Kecman V. Learning and Soft Computing Cambridge, MA: MIT Press; 2001.

339. Keim DA. Visual techniques for exploring databases. In: Tutorial Notes, 3rd Int Conf Knowledge Discovery and Data Mining (KDD’97). Newport Beach, CA. Aug. 1997.

340. Kerber R. ChiMerge: Discretization of numeric attributes. In: Proc 1992 Nat Conf Artificial Intelligence (AAAI’92). San Jose, CA. 1992;123–128.

341. Koller D, Friedman N. Probabilistic Graphical Models: Principles and Techniques Cambridge, MA: MIT Press; 2009.

342. Koperski K, Han J. Discovery of spatial association rules in geographic information databases. In: Proc 1995 Int Symp Large Spatial Databases (SSD’95). Portland, ME. Aug. 1995;47–66.

343. Kononenko I, Hong SJ. Attribute selection for modeling. Future Generation Computer Systems. 1997;13:181–195.

344. Kim M-S, Han J. A particle-and-density based evolutionary clustering method for dynamic networks. In: Proc 2009 Int Conf Very Large Data Bases (VLDB’09). Lyon, France. Aug. 2009.

345. Kamber M, Han J, Chiang JY. Metarule-guided mining of multi-dimensional association rules using data cubes. In: Proc 1997 Int Conf Knowledge Discovery and Data Mining (KDD’97). Newport Beach, CA. Aug. 1997;207–210.

346. Karypis G, Han E-H, Kumar V. CHAMELEON: A hierarchical clustering algorithm using dynamic modeling. COMPUTER. 1999;32:68–75.

347. Kargupta H, Han J, Yu PS, Motwani R, Kumar V. Next Generation of Data Mining Chapman & Hall/CRC 2008.

348. Kohavi R, John GH. Wrappers for feature subset selection. Artificial Intelligence. 1997;97:273–324.

349. Kargupta H, Joshi A, Sivakumar K, Yesha Y. Data Mining: Next Generation Challenges and Future Directions Cambridge, MA: AAAI/MIT Press; 2004.

350. Kuramochi M, Karypis G. Frequent subgraph discovery. In: Proc 2001 Int Conf Data Mining (ICDM’01). San Jose, CA. Nov. 2001;313–320.

351. Kim HS, Kim S, Weninger T, Han J, Abdelzaher T. NDPMine: Efficiently mining discriminative numerical features for pattern-based classification. In: Proc 2010 European Conf Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD’10). Barcelona, Spain. Sept. 2010.

352. Kriegel H-P, Kroeger P, Zimek A. Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Trans Knowledge Discovery from Data (TKDD). 2009;3(1):1–58.

353. Khan M, Le H, Ahmadi H, Abdelzaher T, Han J. DustMiner: Troubleshooting interactive complexity bugs in sensor networks. In: Proc 2008 ACM Int Conf Embedded Networked Sensor Systems (SenSys’08). Raleigh, NC. Nov. 2008;99–112.

354. Kleinberg JM. Authoritative sources in a hyperlinked environment. J ACM. 1999;46:604–632.

355. Kennedy RL, Lee Y, Van Roy B, Reed CD, Lippman RP. Solving Data Mining Problems Through Pattern Recognition Prentice-Hall 1998.

356. Kodratoff Y, Michalski RS. Machine Learning, An Artificial Intelligence Approach Morgan Kaufmann 1990; Vol. 3.

357. Kivinen J, Mannila H. The power of sampling in knowledge discovery. In: Proc 13th ACM Symp Principles of Database Systems. Minneapolis, MN. May 1994;77–85.

358. Kanungo T, Mount DM, Netanyahu NS, Piatko CD, Silverman R, Wu AY. An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans Pattern Analysis and Machine Intelligence (PAMI). 2002;24:881–892.

359. Klemettinen M, Mannila H, Ronkainen P, Toivonen H, Verkamo AI. Finding interesting rules from large sets of discovered association rules. In: Proc 3rd Int Conf Information and Knowledge Management. Gaithersburg, MD. Nov. 1994;401–408.

360. Kubica J, Moore A, Schneider J. Tractable group detection on large link data sets. In: Proc 2003 Int Conf Data Mining (ICDM’03). Melbourne, FL. Nov. 2003;573–576.

361. Knorr E, Ng R. A unified notion of outliers: Properties and computation. In: Proc 1997 Int Conf Knowledge Discovery and Data Mining (KDD’97). Newport Beach, CA. Aug. 1997;219–222.

362. Kutner MH, Nachtsheim CJ, Neter J, Li W. Applied Linear Statistical Models with Student CD Irwin 2004.

363. Knorr EM, Ng RT, Tucakov V. Distance-based outliers: Algorithms and applications. The VLDB J. 2000;8:237–253.

364. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Montreal, Quebec, Canada Aug. 1995;1137–1143. Proc 14th Joint Int Conf Artificial Intelligence (IJCAI’95). Vol. 2.

365. Kolodner JL. Case-Based Reasoning Morgan Kaufmann 1993.

366. Kononenko I. On biases in estimating multi-valued attributes. In: Montreal, Quebec, Canada Aug. 1995;1034–1040. Proc 14th Joint Int Conf Artificial Intelligence (IJCAI’95). Vol. 2.

367. Koton P. Reasoning about evidence in causal explanation. In: Proc 7th Nat Conf Artificial Intelligence (AAAI’88). St. Paul, MN. Aug. 1988;256–263.

368. Kleinberg JM, Papadimitriou C, Raghavan P. A microeconomic view of data mining. Data Mining and Knowledge Discovery. 1998;2:311–324.

369. Karp RM, Papadimitriou CH, Shenker S. A simple algorithm for finding frequent elements in streams and bags. ACM Trans Database Systems. 2003;28:51–55.

370. Kaufman L, Rousseeuw PJ. Finding Groups in Data: An Introduction to Cluster Analysis John Wiley & Sons 1990.

371. Kimball R, Ross M. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling 2nd ed. John Wiley & Sons 2002.

372. Krane D, Raymer R. Fundamental Concepts of Bioinformatics Benjamin Cummings 2003.

373. Krebs V. Mapping networks of terrorist cells. Connections. 2002;24:43–52 (Winter).

374. Kumar R, Raghavan P, Rajagopalan S, Sivakumar D, Tomkins A, Upfal E. Stochastic models for the web graph. In: Proc 2000 IEEE Symp Foundations of Computer Science (FOCS’00). Redondo Beach, CA. Nov. 2000;57–65.

375. Kimball R, Ross M, Thornthwaite W, Mundy J. The Data Warehouse Lifecycle Toolkit Hoboken, NJ: John Wiley & Sons; 2008.

376. Kriegel H-P, Schubert M, Zimek A. Angle-based outlier detection in high-dimensional data. In: Proc 2008 ACM SIGKDD Int Conf Knowledge Discovery and Data Mining (KDD’08). Las Vegas, NV. Aug. 2008;444–452.

377. Kleinberg JM, Tomkins A. Application of linear algebra in information retrieval and hypertext analysis. In: Proc 18th ACM Symp Principles of Database Systems (PODS’99). Philadelphia, PA. May 1999;185–193.

378. Korf I, Yandell M, Bedell J. BLAST Sebastopol, CA: O’Reilly Media; 2003.

379. Lam W. Bayesian network refinement via machine learning approach. IEEE Trans Pattern Analysis and Machine Intelligence. 1998;20:240–252.

380. Lauritzen SL. The EM algorithm for graphical association models with missing data. Computational Statistics and Data Analysis. 1995;19:191–201.

381. Lo D, Cheng H, Han J, Khoo S, Sun C. Classification of software behaviors for failure detection: A discriminative pattern mining approach. In: Proc 2009 ACM SIGKDD Int Conf Knowledge Discovery and Data Mining (KDD’09). Paris, France. June 2009;557–566.

382. Lin CX, Ding B, Han J, Zhu F, Zhao B. Text cube: Computing IR measures for multidimensional text database analysis. In: Proc 2008 Int Conf Data Mining (ICDM’08). Pisa, Italy. Dec. 2008;905–910.

383. Li Z, Ding B, Han J, Kays R, Nye P. Mining periodic behaviors for moving objects. In: Proc 2010 ACM SIGKDD Conf Knowledge Discovery and Data Mining (KDD’10). Washington, DC. July 2010;1099–1108.

384. Li J, Dong G, Ramamohanrarao K. Making use of the most expressive jumping emerging patterns for classification. In: Proc 2000 Pacific-Asia Conf Knowledge Discovery and Data Mining (PAKDD’00). Kyoto, Japan. Apr. 2000;220–232.

385. Le Cun Y, Denker JS, Solla SA. Optimal brain damage. In: Touretzky D, ed. Advances in Neural Information Processing Systems. Morgan Kaufmann. 1990.

386. Leake DB. CBR in context: The present and future. In: Leake DB, ed. Cased-Based Reasoning: Experiences, Lessons, and Future Directions. AAAI Press. 1996;3–30.

387. Lawrence S, Giles CL, Tsoi AC. Symbolic conversion, grammatical inference and rule extraction for foreign exchange rate prediction. In: Abu-Mostafa Y, Weigend AS, Refenes PN, eds. Neural Networks in the Capital Markets. London: World Scientific; 1997.

388. Liu B, Hsu W, Chen S. Using general impressions to analyze discovered classification rules. In: Proc 1997 Int Conf Knowledge Discovery and Data Mining (KDD’97). Newport Beach, CA. Aug. 1997;31–36.

389. Lu H, Han J, Feng L. Stock movement and n-dimensional inter-transaction association rules. In: Proc 1998 SIGMOD Workshop Research Issues on Data Mining and Knowledge Discovery (DMKD’98). Seattle, WA. June 1998;12:1–12:7.

390. Li X, Han J, Gonzalez H. High-dimensional OLAP: A minimal cubing approach. In: Proc 2004 Int Conf Very Large Data Bases (VLDB’04). Toronto, Ontario, Canada. Aug. 2004;528–539.

391. Li X, Han J, Kim S, Gonzalez H. Roam: Rule- and motif-based anomaly detection in massive moving object data sets. In: Proc 2007 SIAM Int Conf Data Mining (SDM’07). Minneapolis, MN. Apr. 2007.

392. Liu B, Hsu W, Ma Y. Integrating classification and association rule mining. In: Proc 1998 Int Conf Knowledge Discovery and Data Mining (KDD’98). New York. Aug. 1998;80–86.

393. Li W, Han J, Pei J. CMAR: Accurate and efficient classification based on multiple class-association rules. In: Proc 2001 Int Conf Data Mining (ICDM’01). San Jose, CA. Nov. 2001;369–376.

394. Liu H, Hussain F, Tan CL, Dash M. Discretization: An enabling technique. Data Mining and Knowledge Discovery. 2002;6:393–423.

395. Lee J-G, Han J, Whang K. Clustering trajectory data. In: Proc 2007 ACM-SIGMOD Int Conf Management of Data (SIGMOD’07). Beijing, China. June 2007.

396. Liu H, Han J, Xin D, Shao Z. Mining frequent patterns on very high dimensional data: A top-down row enumeration approach. In: Proc 2006 SIAM Int Conf Data Mining (SDM’06). Bethesda, MD. Apr. 2006.

397. Li X, Han J, Yin Z, Lee J-G, Sun Y. Sampling Cube: A framework for statistical OLAP over sampling data. In: Proc 2008 ACM SIGMOD Int Conf Management of Data (SIGMOD’08). Vancouver, British Columbia, Canada. June 2008;779–790.

398. Liu B. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data New York: Springer; 2006.

399. Laurikkala J, Juhola M, Kentala E. Informal identification of outliers in medical data. In: Proc 5th Int Workshop on Intelligent Data Analysis in Medicine and Pharmacology. Berlin, Germany. Aug. 2000.

400. Lee Y-K, Kim W-Y, Cai YD, Han J. CoMine: Efficient mining of correlated patterns. In: Proc 2003 Int Conf Data Mining (ICDM’03). Melbourne, FL. Nov. 2003;581–584.

401. Leskovec J, Kleinberg J, Faloutsos C. Graphs over time: Densification laws, shrinking diameters and possible explanations. In: Proc 2005 ACM SIGKDD Int Conf Knowledge Discovery and Data Mining (KDD’05). Chicago, IL. Aug. 2005;177–187.

402. Liu G, Lu H, Lou W, Yu JX. On computing, storing and querying frequent patterns. In: Proc 2003 ACM SIGKDD Int Conf Knowledge Discovery and Data Mining (KDD’03). Washington, DC. Aug. 2003;607–612.

403. Li Z, Lu S, Myagmar S, Zhou Y. CP-Miner: A tool for finding copy-paste and related bugs in operating system code. In: Proc 2004 Symp Operating Systems Design and Implementation (OSDI’04). San Francisco, CA. Dec. 2004;20–22.

404. Lloyd SP. Least squares quantization in PCM. IEEE Trans Information Theory. 1982;28:128–137 (original version: Technical Report, Bell Labs, 1957).

405. Lim T-S, Loh W-Y, Shih Y-S. A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning. 2000;40:203–228.

406. Laskey K, Mahoney S. Network fragments: Representing knowledge for constructing probabilistic models. In: Proc 13th Annual Conf Uncertainty in Artificial Intelligence. San Francisco, CA. Aug. 1997;334–341.

407. Liu H, Motoda H. Feature Selection for Knowledge Discovery and Data Mining Kluwer Academic 1998.

408. Liu H, Motoda H, eds. Feature Extraction, Construction, and Selection: A Data Mining Perspective. Kluwer Academic 1998.

409. Lakshmanan LVS, Ng R, Han J, Pang A. Optimization of constrained frequent set queries with 2-variable constraints. In: Proc 1999 ACM-SIGMOD Int Conf Management of Data (SIGMOD’99). Philadelphia, PA. June 1999;157–168.

410. Liben-Nowell D, Kleinberg J. The link prediction problem for social networks. In: Proc 2003 Int Conf Information and Knowledge Management (CIKM’03). New Orleans, LA. Nov. 2003;556–559.

411. Loshin D. Enterprise Knowledge Management: The Data Quality Approach Morgan Kaufmann 2001.

412. Lenarcik A, Piasta Z. Probabilistic rough classifiers with mixture of discrete and continuous variables. In: Lin TY, Cercone N, eds. Rough Sets and Data Mining: Analysis for Imprecise Data. Kluwer Academic 1997;373–383.

413. Lakshmanan LVS, Pei J, Han J. Quotient cube: How to summarize the semantics of a data cube. In: Proc 2002 Int Conf Very Large Data Bases (VLDB’02). Hong Kong, China. Aug. 2002;778–789.

414. Liu J, Pan Y, Wang K, Han J. Mining frequent itemsets by opportunistic projection. In: Proc 2002 ACM SIGKDD Int Conf Knowledge Discovery in Databases (KDD’02). Edmonton, Alberta, Canada. July 2002;239–248.

415. Lakshmanan LVS, Pei J, Zhao Y. QC-Trees: An efficient summary structure for semantic OLAP. In: Proc 2003 ACM-SIGMOD Int Conf Management of Data (SIGMOD’03). San Diego, CA. June 2003;64–75.

416. Liu H, Setiono R. Chi2: Feature selection and discretization of numeric attributes. In: Proc 1995 IEEE Int Conf Tools with AI (ICTAI’95). Washington, DC. Nov. 1995;388–391.

417. Loh WY, Shih YS. Split selection methods for classification trees. Statistica Sinica. 1997;7:815–840.

418. Langley P, Simon HA, Bradshaw GL, Zytkow JM. Scientific Discovery: Computational Explorations of the Creative Processes Cambridge, MA: MIT Press; 1987.

419. Lu H, Setiono R, Liu H. Neurorule: A connectionist approach to data mining. In: Proc 1995 Int Conf Very Large Data Bases (VLDB’95). Zurich, Switzerland. Sept. 1995;478–489.

420. Lent B, Swami A, Widom J. Clustering association rules. In: Proc 1997 Int Conf Data Engineering (ICDE’97). Birmingham, England. Apr. 1997;220–231.

421. Luxburg U. A tutorial on spectral clustering. Statistics and Computing. 2007;17:395–416.

422. Loh WY, Vanichsetakul N. Tree-structured classificaiton via generalized discriminant analysis. J American Statistical Association. 1988;83:715–728.

423. Li Z, Zhou Y. PR-Miner: Automatically extracting implicit programming rules and detecting violations in large software code. In: Proc 2005 ACM SIGSOFT Symp Foundations of Software Engineering (FSE’05). Lisbon, Portugal. Sept. 2005.

424. Mitra S, Acharya T. Data Mining: Multimedia, Soft Computing, and Bioinformatics John Wiley & Sons 2003.

425. Metwally A, Agrawal D, El Abbadi A. Efficient computation of frequent and top-k elements in data streams. In: Proc 2005 Int Conf Database Theory (ICDT’05). Edinburgh, Scotland. Jan. 2005;398–412.

426. MacQueen J. Some methods for classification and analysis of multivariate observations. Proc 5th Berkeley Symp Math Stat Prob. 1967;1:281–297.

427. Magidson J. The CHAID approach to segmentation modeling: CHI-squared automatic interaction detection. In: Bagozzi RP, ed. Advanced Methods of Marketing Research. Blackwell Business 1994;118–159.

428. Mannila H. Theoretical frameworks of data mining. SIGKDD Explorations. 2000;1:30–32.

429. Mehta M, Agrawal R, Rissanen J. SLIQ: A fast scalable classifier for data mining. In: Proc 1996 Int Conf Extending Database Technology (EDBT’96). Avignon, France. Mar. 1996;18–32.

430. Marsland S. Machine Learning: An Algorithmic Perspective Chapman & Hall/CRC 2009.

431. McLachlan GJ, Basford KE. Mixture Models: Inference and Applications to Clustering John Wiley & Sons 1988.

432. Mahoney MV, Chan PK. Learning rules for anomaly detection of hostile network traffic. In: Proc 2003 Int Conf Data Mining (ICDM’03). Melbourne, FL. Nov. 2003.

433. Mamoulis N, Cao H, Kollios G, Hadjieleftheriou M, Tao Y, Cheung D. Mining, indexing, and querying historical spatiotemporal data. In: Proc 2004 ACM SIGKDD Int Conf Knowledge Discovery in Databases (KDD’04). Seattle, WA. Aug. 2004;236–245.

434. Michalski RS, Carbonell JG, Mitchell TM. Machine Learning, An Artificial Intelligence Approach Morgan Kaufmann 1983; Vol. 1.

435. Michalski RS, Carbonell JG, Mitchell TM. Machine Learning, An Artificial Intelligence Approach Morgan Kaufmann 1986; Vol. 2.

436. Muralikrishna M, DeWitt DJ. Equi-depth histograms for extimating selectivity factors for multi-dimensional queries. In: Proc 1988 ACM-SIGMOD Int Conf Management of Data (SIGMOD’88). Chicago, IL. June 1988;28–36.

437. Meilǎ M. Comparing clusterings by the variation of information. In: Proc 16th Annual Conf Computational Learning Theory (COLT’03). Washington, DC. Aug. 2003;173–187.

438. Meilǎ M. Comparing clusterings: An axiomatic view. In: Proc 22nd Int Conf Machine Learning (ICML’05). Bonn, Germany. 2005;577–584.

439. Mena J. Investigative Data Mining with Security and Criminal Detection Butterworth-Heinemann 2003.

440. Malerba D, Floriana E, Semeraro G. A further comparison of simplification methods for decision tree induction. In: Fisher D, Lenz H, eds. Learning from Data: AI and Statistics. Springer Verlag 1995.

441. Martin JK, Hirschberg DS. The time complexity of decision tree induction. In: Technical Report ICS-TR 95-27. Department of Information and Computer Science, University of California, Irvine, CA. Aug. 1995;1–27.

442. Miller H, Han J. Geographic Data Mining and Knowledge Discovery 2nd ed. Chapman & Hall/CRC 2009.

443. Michalski RS. A theory and methodology of inductive learning. In: Michalski RS, Carbonell JG, Mitchell TM, eds. Morgan Kaufmann 1983;83–134. Machine Learning: An Artificial Intelligence Approach. Vol. 1.

444. Michalewicz Z. Genetic Algorithms + Data Structures = Evolution Programs Springer Verlag 1992.

445. Miller RG. Survival Analysis Wiley-Interscience 1998.

446. Mingers J. An empirical comparison of pruning methods for decision-tree induction. Machine Learning. 1989;4:227–243.

447. Mirkin B. Mathematical classification and clustering. J Global Optimization. 1998;12:105–108.

448. Mitchell M. An Introduction to Genetic Algorithms Cambridge, MA: MIT Press; 1996.

449. Mitchell TM. Machine Learning McGraw-Hill 1997.

450. Manago M, Kodratoff Y. Induction of decision trees from complex structured data. In: Piatetsky-Shapiro G, Frawley WJ, eds. Knowledge Discovery in Databases. AAAI/MIT Press 1991;289–306.

451. Mei Q, Liu C, Su H, Zhai C. A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In: Proc 15th Int Conf World Wide Web (WWW’06). Edinburgh, Scotland. May 2006;533–542.

452. Major J, Mangano J. Selecting among rules induced from a hurricane database. J Intelligent Information Systems. 1995;4:39–52.

453. Manku G, Motwani R. Approximate frequency counts over data streams. In: Proc 2002 Int Conf Very Large Data Bases (VLDB’02). Hong Kong, China. Aug. 2002;346–357.

454. Mézard M, Nadal J-P. Learning in feedforward layered networks: The tiling algorithm. J Physics. 1989;22:2191–2204.

455. Madeira SC, Oliveira AL. Biclustering algorithms for biological data analysis: A survey. IEEE/ACM Trans Computational Biology and Bioinformatics. 2004;1(1):24–25.

456. Minsky ML, Papert S. Perceptrons: An Introduction to Computational Geometry Cambridge, MA: MIT Press; 1969.

457. Metha M, Rissanen J, Agrawal R. MDL-based decision tree pruning. In: Proc 1995 Int Conf Knowledge Discovery and Data Mining (KDD’95). Montreal, Quebec, Canada. Aug. 1995;216–221.

458. Manning CD, Raghavan P, Schutze H. Introduction to Information Retrieval Cambridge University Press 2008.

459. Markou M, Singh S. Novelty detection: A review—part 1: Statistical approaches. Signal Processing. 2003;83:2481–2497.

460. Markou M, Singh S. Novelty detection: A review—part 2: Neural network based approaches. Signal Processing. 2003;83:2499–2521.

461. Michie D, Spiegelhalter DJ, Taylor CC. Machine Learning, Neural and Statistical Classification Chichester, England: Ellis Horwood; 1994.

462. Michalski RS, Tecuci G. Machine Learning, A Multistrategy Approach Morgan Kaufmann 1994; Vol. 4.

463. Mannila H, Toivonen H, Verkamo AI. Efficient algorithms for discovering association rules. In: Proc AAAI’94 Workshop Knowledge Discovery in Databases (KDD’94). Seattle, WA. July 1994;181–192.

464. Mannila H, Toivonen H, Verkamo AI. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery. 1997;1:259–289.

465. Murthy SK. Automatic construction of decision trees from data: A multi-disciplinary survey. Data Mining and Knowledge Discovery. 1998;2:345–389.

466. S. Muthukrishnan. Data Streams: Algorithms and Applications. Now Publishers, 2005.

467. Mei Q, Xin D, Cheng H, Han J, Zhai C. Semantic annotation of frequent patterns. ACM Trans Knowledge Discovery from Data (TKDD). 2007;15:321–348.

468. Miller RJ, Yang Y. Association rules over interval data. In: Proc 1997 ACM-SIGMOD Int Conf Management of Data (SIGMOD’97). Tucson, AZ. May 1997;452–461.

469. Mei Q, Zhai C. A mixture model for contextual text mining. In: Proc 2006 ACM SIGKDD Int Conf Knowledge Discovery in Databases (KDD’06). Philadelphia, PA. Aug. 2006;649–655.

470. Niblett T, Bratko I. Learning decision rules in noisy domains. In: Brammer MA, ed. Expert Systems ’86: Research and Development in Expert Systems III. British Computer Society Specialist Group on Expert Systems Dec. 1986;25–34.

471. Newman M, Barabasi A-L, Watts DJ. The Structure and Dynamics of Networks Princeton University Press 2006.

472. Noble CC, Cook DJ. Graph-based anomaly detection. In: Proc 2003 ACM SIGKDD Int Conf Knowledge Discovery and Data Mining (KDD’03). Washington, DC. Aug. 2003;631–636.

473. Newman M. Networks: An Introduction Oxford University Press 2010.

474. Newman MEJ, Girvan M. Finding and evaluating community structure in networks. Physical Rev E. 2004;69:113–128.

475. Neville J, Gallaher B, Eliassi-Rad T. Evaluating statistical tests for within-network classifiers of relational data. In: Proc 2009 Int Conf Data Mining (ICDM’09). Miami, FL. Dec. 2009;397–406.

476. Ng R, Han J. Efficient and effective clustering method for spatial data mining. In: Proc 1994 Int Conf Very Large Data Bases (VLDB’94). Santiago, Chile. Sept. 1994;144–155.

477. Ng AY, Jordan MI, Weiss Y. On spectral clustering: Analysis and an algorithm. In: Dietterich TG, Becker S, Ghahramani Z, eds. Advances in Neural Information Processing Systems 14. Cambridge, MA: MIT Press; 2001;849–856.

478. Nijssen S, Kok J. A quick start in frequent structure mining can make a difference. In: Proc 2004 ACM SIGKDD Int Conf Knowledge Discovery in Databases (KDD’04). Seattle, WA. Aug. 2004;647–652.

479. Neter J, Kutner MH, Nachtsheim CJ, Wasserman L. Applied Linear Statistical Models 4th ed. Irwin 1996.

480. Ng R, Lakshmanan LVS, Han J, Pang A. Exploratory mining and pruning optimizations of constrained associations rules. In: Proc 1998 ACM-SIGMOD Int Conf Management of Data (SIGMOD’98). Seattle, WA. June 1998;13–24.

481. Natsev A, Rastogi R, Shim K. Walrus: A similarity retrieval algorithm for image databases. In: Proc 1999 ACM-SIGMOD Int Conf Management of Data (SIGMOD’99). Philadelphia, PA. June 1999;395–406.

482. Nocedal J, Wright SJ. Numerical Optimization Springer Verlag 1999.

483. Osuna E, Freund R, Girosi F. An improved training algorithm for support vector machines. In: Proc 1997 IEEE Workshop Neural Networks for Signal Processing (NNSP’97). Amelia Island, FL. Sept. 1997;276–285.

484. O’Neil P, Graefe G. Multi-table joins through bitmapped join indices. SIGMOD Record. Sept. 1995;24:8–11.

485. Olson JE. Data Quality: The Accuracy Dimension Morgan Kaufmann 2003.

486. Omiecinski E. Alternative interest measures for mining associations. IEEE Trans Knowledge and Data Engineering. 2003;15:57–69.

487. O’Callaghan L, Meyerson A, Motwani R, Mishra N, Guha S. Streaming-data algorithms for high-quality clustering. In: Proc 2002 Int Conf Data Engineering (ICDE’02). San Fransisco, CA. Apr. 2002;685–696.

488. O’Neil P, Quass D. Improved query performance with variant indexes. In: Proc 1997 ACM-SIGMOD Int Conf Management of Data (SIGMOD’97). Tucson, AZ. May 1997;38–49.

489. Özden B, Ramaswamy S, Silberschatz A. Cyclic association rules. In: Proc 1998 Int Conf Data Engineering (ICDE’98). Orlando, FL. Feb. 1998;412–421.

490. Pagallo G. Learning DNF by decision trees. In: Proc 1989 Int Joint Conf Artificial Intelligence (IJCAI’89). San Francisco, CA. 1989;639–644.

491. Pawlak Z. Rough Sets, Theoretical Aspects of Reasoning about Data Kluwer Academic 1991.

492. Pinheiro JC, Bates DM. Mixed Effects Models in S and S-PLUS Springer Verlag 2000.

493. Pasquier N, Bastide Y, Taouil R, Lakhal L. Discovering frequent closed itemsets for association rules. In: Proc 7th Int Conf Database Theory (ICDT’99). Jerusalem, Israel. Jan. 1999;398–416.

494. Pan F, Cong G, Tung AKH, Yang J, Zaki M. CARPENTER: Finding closed patterns in long biological datasets. In: Proc 2003 ACM SIGKDD Int Conf Knowledge Discovery and Data Mining (KDD’03). Washington, DC. Aug. 2003;637–642.

495. Park JS, Chen MS, Yu PS. An effective hash-based algorithm for mining association rules. In: Proc 1995 ACM-SIGMOD Int Conf Management of Data (SIGMOD’95). San Jose, CA. May 1995;175–186.

496. Park JS, Chen MS, Yu PS. Efficient parallel mining for association rules. In: Proc 4th Int Conf Information and Knowledge Management. Baltimore, MD. Nov. 1995;31–36.

497. Pearl J. Probabilistic Reasoning in Intelligent Systems Morgan Kaufmann 1988.

498. Pei J, Han J, Lakshmanan LVS. Mining frequent itemsets with convertible constraints. In: Proc 2001 Int Conf Data Engineering (ICDE’01). Heidelberg, Germany. Apr. 2001;433–442.

499. Pei J, Han J, Lu H, Nishio S, Tang S, Yang D. H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases. In: Proc 2001 Int Conf Data Mining (ICDM’01). San Jose, CA. Nov. 2001;441–448.

500. Parsons L, Haque E, Liu H. Subspace clustering for high dimensional data: A review. SIGKDD Explorations. 2004;6:90–105.

501. Pei J, Han J, Mao R. CLOSET: An efficient algorithm for mining frequent closed itemsets. In: Proc 2000 ACM-SIGMOD Int Workshop Data Mining and Knowledge Discovery (DMKD’00). Dallas, TX. May 2000;11–20.

502. Pei J, Han J, Mortazavi-Asl B, et al. PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: Proc 2001 Int Conf Data Engineering (ICDE’01). Heidelberg, Germany. Apr. 2001;215–224.

503. Pei J, Han J, Mortazavi-Asl B, et al. Mining sequential patterns by pattern-growth: The prefixSpan approach. IEEE Trans Knowledge and Data Engineering. 2004;16:1424–1440.

504. Poosala V, Ioannidis Y. Selectivity estimation without the attribute value independence assumption. In: Proc 1997 Int Conf Very Large Data Bases (VLDB’97). Athens, Greece. Aug. 1997;486–495.

505. Papadimitriou S, Kitagawa H, Gibbons PB, Faloutsos C. Loci: Fast outlier detection using the local correlation integral. In: Proc 2003 Int Conf Data Engineering (ICDE’03). Bangalore, India. Mar. 2003;315–326.

506. Pfeffer A, Koller D, Milch B, Takusagawa K. SPOOK: A system for probabilistic object-oriented knowledge representation. In: Proc 15th Annual Conf Uncertainty in Artificial Intelligence (UAI’99). Stockholm, Sweden. 1999;541–550.

507. Papadias D, Kalnis P, Zhang J, Tao Y. Efficient OLAP operations in spatial data warehouses. In: Proc 2001 Int Symp Spatial and Temporal Databases (SSTD’01). Redondo Beach, CA. July 2001;443–459.

508. Pang B, Lee L. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval. 2007;2:1–135.

509. Platt JC. Fast training of support vector machines using sequential minimal optimization. In: Schölkopf B, Burges CJC, Smola A, eds. Advances in Kernel Methods—Support Vector Learning. Cambridge, MA: MIT Press; 1998;185–208.

510. Patcha A, Park J-M. An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks. 2007;51(12):3448–3470.

511. Preparata FP, Shamos MI. Computational Geometry: An Introduction Springer Verlag 1985.

512. Piatetsky-Shapiro G. Notes AAAI’91 Workshop Knowledge Discovery in Databases (KDD’91) Anaheim, CA July 1991.

513. Piatetsky-Shapiro G, Frawley WJ. Knowledge Discovery in Databases AAAI/MIT Press 1991.

514. Pan F, Tung AKH, Cong G, Xu X. COBBLER: Combining column and row enumeration for closed pattern discovery. In: Proc 2004 Int Conf Scientific and Statistical Database Management (SSDBM’04). Santorini Island, Greece. June 2004;21–30.

515. Press WH, Teukolosky SA, Vetterling WT, Flannery BP. Numerical Recipes: The Art of Scientific Computing Cambridge: Cambridge University Press; 2007.

516. Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowledge and Data Engineering. 2010;22:1345–1359.

517. Pyle D. Data Preparation for Data Mining Morgan Kaufmann 1999.

518. Pei J, Zhang X, Cho M, Wang H, Yu PS. Maple: A fast algorithm for maximal pattern-based clustering. In: Proc 2003 Int Conf Data Mining (ICDM’03). Melbourne, FL. Dec. 2003;259–266.

519. Quinlan JR, Cameron-Jones RM. FOIL: A midterm report. In: Proc 1993 European Conf Machine Learning (ECML’93). Vienna, Austria. 1993;3–20.

520. Quinlan JR, Rivest RL. Inferring decision trees using the minimum description length principle. Information and Computation. Mar. 1989;80:227–248.

521. Quinlan JR. Induction of decision trees. Machine Learning. 1986;1:81–106.

522. Quinlan JR. Simplifying decision trees. Int J Man-Machine Studies. 1987;27:221–234.

523. Quinlan JR. An empirical comparison of genetic and decision-tree classifiers. In: Proc 1988 Int Conf Machine Learning (ICML’88). Ann Arbor, MI. June 1988;135–141.

524. Quinlan JR. Unknown attribute values in induction. In: Proc 1989 Int Conf Machine Learning (ICML’89). Ithaca, NY. June 1989;164–168.

525. Quinlan JR. Learning logic definitions from relations. Machine Learning. 1990;5:139–166.

526. Quinlan JR. C4.5: Programs for Machine Learning Morgan Kaufmann 1993.

527. Quinlan JR. Bagging, boosting, and C4.5. In: Portland, OR. Aug. 1996;725–730. Proc 1996 Nat Conf Artificial Intelligence (AAAI’96). Vol. 1.

528. Rissland EL, Ashley K. HYPO: A case-based system for trade secret law. In: Proc 1st Int Conf Artificial Intelligence and Law. Boston, MA. May 1987;60–66.

529. Rabiner LR. A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE. 1989;77:257–286.

530. Russell S, Binder J, Koller D, Kanazawa K. Local learning in probabilistic networks with hidden variables. In: Proc 1995 Joint Int Conf Artificial Intelligence (IJCAI’95). Montreal, Quebec, Canada. Aug. 1995;1146–1152.

531. Ramakrishnan R, Chen B-C. Exploratory mining in cube space. Data Mining and Knowledge Discovery. 2007;15:29–54.

532. Redman T. Data Quality: Management and Technology Bantam Books 1992.

533. Redman T. Data Quality: The Field Guide Digital Press (Elsevier) 2001.

534. Ramakrishnan R, Gehrke J. Database Management Systems 3rd ed. McGraw-Hill 2003.

535. De Raedt L, Guns T, Nijssen S. Constraint programming for data mining and machine learning. In: Proc 2010 AAAI Conf Artificial Intelligence (AAAI’10). Atlanta, GA. July 2010;1671–1675.

536. Raman V, Hellerstein JM. Potter’s wheel: An interactive data cleaning system. In: Proc 2001 Int Conf Very Large Data Bases (VLDB’01). Rome, Italy. Sept. 2001;381–390.

537. Rosenberg A, Hirschberg J. V-measure: A conditional entropy-based external cluster evaluation measure. In: Proc 2007 Joint Conf Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL’07). Prague, Czech Republic. June 2007;410–420.

538. Roddick JF, Hornsby K, Spiliopoulou M. An updated bibliography of temporal, spatial, and spatio-temporal data mining research. In: Roddick JF, Hornsby K, eds. Lecture Notes in Computer Science 2007. New York: Springer; 2001;147–163. TSDM 2000.

539. Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL, eds. Parallel Distributed Processing. Cambridge, MA: MIT Press; 1986.

540. Ripley BD. Pattern Recognition and Neural Networks Cambridge University Press 1996.

541. Rumelhart DE, McClelland JL. Parallel Distributed Processing Cambridge, MA: MIT Press; 1986.

542. Ramaswamy S, Mahajan S, Silberschatz A. On the discovery of interesting patterns in association rules. In: Proc 1998 Int Conf Very Large Data Bases (VLDB’98). New York. Aug. 1998;368–379.

543. Russell S, Norvig P. Artificial Intelligence: A Modern Approach Prentice-Hall 1995.

544. Radovanović M, Nanopoulos A, Ivanović M. Nearest neighbors in high-dimensional data: The emergence and influence of hubs. In: Proc 2009 Int Conf Machine Learning (ICML’09). Montreal, Quebec, Canada. June 2009;865–872.

545. Rosenblatt F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Rev. 1958;65:386–498.

546. Riesbeck C, Schank R. Inside Case-Based Reasoning Lawrence Erlbaum 1989.

547. Ross K, Srivastava D. Fast computation of sparse datacubes. In: Proc 1997 Int Conf Very Large Data Bases (VLDB’97). Athens, Greece. Aug. 1997;116–125.

548. Rastogi R, Shim K. Public: A decision tree classifer that integrates building and pruning. In: Proc 1998 Int Conf Very Large Data Bases (VLDB’98). New York. Aug. 1998;404–415.

549. Ramsey F, Schafer D. The Statistical Sleuth: A Course in Methods of Data Analysis Duxbury Press 2001.

550. Ross KA, Srivastava D, Chatziantoniou D. Complex aggregation at multiple granularities. In: Proc Int Conf Extending Database Technology (EDBT’98). Valencia, Spain. Mar. 1998;263–277.

551. Russ JC. The Image Processing Handbook 5th ed. CRC Press 2006.

552. Srikant R, Agrawal R. Mining generalized association rules. In: Proc 1995 Int Conf Very Large Data Bases (VLDB’95). Zurich, Switzerland. Sept. 1995;407–419.

553. Srikant R, Agrawal R. Mining sequential patterns: Generalizations and performance improvements. In: Proc 5th Int Conf Extending Database Technology (EDBT’96). Avignon, France. Mar. 1996;3–17.

554. Shafer J, Agrawal R, Mehta M. SPRINT: A scalable parallel classifier for data mining. In: Proc 1996 Int Conf Very Large Data Bases (VLDB’96). Bombay, India. Sept. 1996;544–555.

555. Sarawagi S, Agrawal R, Megiddo N. Discovery-driven exploration of OLAP data cubes. In: Proc Int Conf Extending Database Technology (EDBT’98). Valencia, Spain. Mar. 1998;168–182.

556. Schölkopf B, Bartlett PL, Smola A, Williamson R. Shrinking the tube: A new support vector regression algorithm. In: Kearns MS, Solla SA, Cohn DA, eds. Advances in Neural Information Processing Systems 11. Cambridge, MA: MIT Press; 1999;330–336.

557. Shekhar S, Chawla S. Spatial Databases: A Tour Prentice-Hall 2003.

558. Schlimmer JC. Learning and representation change. In: Proc 1986 Nat Conf Artificial Intelligence (AAAI’86). Philadelphia, PA. 1986;511–515.

559. Schaeffer SE. Graph clustering. Computer Science Rev. 2007;1:27–64.

560. Sheikholeslami G, Chatterjee S, Zhang A. WaveCluster: A multi-resolution clustering approach for very large spatial databases. In: Proc 1998 Int Conf Very Large Data Bases (VLDB’98). New York. Aug. 1998;428–439.

561. Shavlik JW, Dietterich TG. Readings in Machine Learning Morgan Kaufmann 1990.

562. Soukup T, Davidson I. Visual Data Mining: Techniques and Tools for Data Visualization and Mining Wiley 2002.

563. Srivastava D, Dar S, Jagadish HV, Levy AV. Answering queries with aggregation using views. In: Proc 1996 Int Conf Very Large Data Bases (VLDB’96). Bombay, India. Sept. 1996;318–329.

564. Shukla A, Deshpande PM, Naughton JF. Materialized view selection for multidimensional datasets. In: Proc 1998 Int Conf Very Large Data Bases (VLDB’98). New York. Aug. 1998;488–499.

565. Seni G, Elder JF. Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions Morgan and Claypool 2010.

566. Settles B. Active learning literature survey. In: Computer Sciences Technical Report 1648. University of Wisconsin–Madison 2010.

567. Schlimmer JC, Fisher D. A case study of incremental concept induction. In: Proc 1986 Nat Conf Artificial Intelligence (AAAI’86). Philadelphia, PA. 1986;496–501.

568. Shanmugasundaram J, Fayyad UM, Bradley PS. Compressed data cubes for OLAP aggregate query approximation on continuous dimensions. In: Proc 1999 Int Conf Knowledge Discovery and Data Mining (KDD’99). San Diego, CA. Aug. 1999;223–232.

569. Smyth P, Goodman RM. An information theoretic approach to rule induction. IEEE Trans Knowledge and Data Engineering. 1992;4:301–316.

570. Shewhart WA. Economic Control of Quality of Manufactured Product D. Van Nostrand 1931.

571. Shih Y-S. Families of splitting criteria for classification trees. Statistics and Computing. 1999;9:309–315.

572. Stefanovic N, Han J, Koperski K. Object-based selective materialization for efficient implementation of spatial data cubes. IEEE Trans Knowledge and Data Engineering. 2000;12:938–958.

573. Shoshani A. OLAP and statistical databases: Similarities and differences. In: Proc 16th ACM Symp Principles of Database Systems. Tucson, AZ. May 1997;185–196.

574. Shumway RH. Applied Statistical Time Series Analysis Prentice-Hall 1988.

575. Shao Z, Han J, Xin D. MM-Cubing: Computing iceberg cubes by factorizing the lattice space. In: Proc 2004 Int Conf Scientific and Statistical Database Management (SSDBM’04). Santorini Island, Greece. June 2004;213–222.

576. Sun Y, Han J, Zhao P, Yin Z, Cheng H, Wu T. RankClus: Integrating clustering with ranking for heterogeneous information network analysis. In: Proc 2009 Int Conf Extending Data Base Technology (EDBT’09). Saint Petersburg, Russia. Mar. 2009;565–576.

577. Silvestri F. Mining query logs: Turning search usage data into knowledge. Foundations and Trends in Information Retrieval. 2010;4:1–174.

578. Shieh J, Keogh E. iSAX: Indexing and mining terabyte sized time series. In: Proc 2008 ACM SIGKDD Int Conf Knowledge Discovery and Data Mining (KDD’08). Las Vegas, NV. Aug. 2008;623–631.

579. Silberschatz A, Korth HF, Sudarshan S. Database System Concepts 6th ed. McGraw-Hill 2010.

580. Shekhar S, Lu C-T, Tan X, Chawla S, Vatsavai RR. Map cube: A visualization tool for spatial data warehouses. In: Miller HJ, Han J, eds. Geographic Data Mining and Knowledge Discovery. Taylor and Francis 2001;73–108.

581. Setubal JC, Meidanis J. Introduction to Computational Molecular Biology PWS Publishing Co. 1997.

582. Shavlik JW, Mooney RJ, Towell GG. Symbolic and neural learning algorithms: An experimental comparison. Machine Learning. 1991;6:111–144.

583. Saito K, Nakano R. Medical diagnostic expert system based on PDP model. In: Proc 1988 IEEE Int Conf Neural Networks. San Mateo, CA. 1988;225–262.

584. Shen W, Ong K, Mitbander B, Zaniolo C. Metaqueries for data mining. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R, eds. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press 1996;375–398.

585. Savasere A, Omiecinski E, Navathe S. An efficient algorithm for mining association rules in large databases. In: Proc 1995 Int Conf Very Large Data Bases (VLDB’95). Zurich, Switzerland. Sept. 1995;432–443.

586. Savasere A, Omiecinski E, Navathe S. Mining for strong negative associations in a large database of customer transactions. In: Proc 1998 Int Conf Data Engineering (ICDE’98). Orlando, FL. Feb. 1998;494–502.

587. Sokal R, Rohlf F. Biometry Freeman 1981.

588. Skowron A, Rauszer C. The discernibility matrices and functions in information systems. In: Slowinski R, ed. Intelligent Decision Support, Handbook of Applications and Advances of the Rough Set Theory. Kluwer Academic 1992;331–362.

589. Siedlecki W, Sklansky J. On automatic feature selection. Int J Pattern Recognition and Artificial Intelligence. 1988;2:197–220.

590. Sarawagi S, Stonebraker M. Efficient organization of large multidimensional arrays. In: Proc 1994 Int Conf Data Engineering (ICDE’94). Houston, TX. Feb. 1994;328–336.

591. Sathe G, Sarawagi S. Intelligent rollups in multidimensional OLAP data. In: Proc 2001 Int Conf Very Large Data Bases (VLDB’01). Rome, Italy. Sept. 2001;531–540.

592. Shumway RH, Stoffer DS. Time Series Analysis and Its Applications New York: Springer; 2005.

593. Silberschatz A, Tuzhilin A. What makes patterns interesting in knowledge discovery systems. IEEE Trans Knowledge and Data Engineering. Dec. 1996;8:970–974.

594. Sarawagi S, Thomas S, Agrawal R. Integrating association rule mining with relational database systems: Alternatives and implications. In: Proc 1998 ACM-SIGMOD Int Conf Management of Data (SIGMOD’98). Seattle, WA. June 1998;343–354.

595. Sun Y, Tang J, Han J, Gupta M, Zhao B. Community evolution detection in dynamic heterogeneous information networks. In: Proc 2010 KDD Workshop Mining and Learning with Graphs (MLG’10). Washington, DC. July 2010.

596. Stefansky W. Rejecting outliers in factorial designs. Technometrics. 1972;14:469–479.

597. Stone M. Cross-validatory choice and assessment of statistical predictions. J Royal Statistical Society. 1974;36:111–147.

598. Srikant R, Vu Q, Agrawal R. Mining association rules with item constraints. In: Proc 1997 Int Conf Knowledge Discovery and Data Mining (KDD’97). Newport Beach, CA. Aug. 1997;67–73.

599. Shannon CE, Weaver W. The Mathematical Theory of Communication University of Illinois Press 1949.

600. Swets J. Measuring the accuracy of diagnostic systems. Science. 1988;240:1285–1293.

601. Swiniarski R. Rough sets and principal component analysis and their applications in feature extraction and selection, data model building and classification. In: Pal SK, Skowron A, eds. Rough Fuzzy Hybridization: A New Trend in Decision-Making. Singapore: Springer Verlag; 1999.

602. Song X, Wu M, Jermaine C, Ranka S. Conditional anomaly detection. IEEE Trans on Knowledge and Data Engineering. 2007;19(5):631–645.

603. Shasha D, Zhu Y. High Performance Discovery in Time Series: Techniques and Case Studies New York: Springer; 2004.

604. Tax DMJ, Duin RPW. Using two-class classifiers for multiclass classification. In: Proc 16th Intl Conf Pattern Recognition (ICPR’2002). Montreal, Quebec, Canada. 2002;124–127.

605. Tao Y, Faloutsos C, Papadias D, Liu B. Prediction and indexing of moving objects with unknown motion patterns. In: Proc 2004 ACM-SIGMOD Int Conf Management of Data (SIGMOD’04). Paris, France. June 2004;611–622.

606. Tsoukatos I, Gunopulos D. Efficient mining of spatiotemporal patterns. In: Proc 2001 Int Symp Spatial and Temporal Databases (SSTD’01). Redondo Beach, CA. July 2001;425–442.

607. Tung AKH, Hou J, Han J. Spatial clustering in the presence of obstacles. In: Proc 2001 Int Conf Data Engineering (ICDE’01). Heidelberg, Germany. Apr. 2001;359–367.

608. Tung AKH, Han J, Lakshmanan LVS, Ng RT. Constraint-based clustering in large databases. In: Proc 2001 Int Conf Database Theory (ICDT’01). London. Jan. 2001;405–419.

609. Tian Y, Hankins RA, Patel JM. Efficient aggregation for graph summarization. In: Proc 2008 ACM SIGMOD Int Conf Management of Data (SIGMOD’08). Vancouver, British Columbia, Canada. June 2008;567–580.

610. Thuraisingham B. Data mining for counterterrorism. In: Kargupta H, Joshi A, Sivakumar K, Yesha Y, eds. Data Mining: Next Generation Challenges and Future Directions. AAAI/MIT Press 2004;157–183.

611. Theodoridis S, Koutroumbas K. Pattern Recognition 4th ed. Academic Press 2008.

612. Tan P-N, Kumar V, Srivastava J. Selecting the right interestingness measure for association patterns. In: Proc 2002 ACM SIGKDD Int Conf Knowledge Discovery in Databases (KDD’02). Edmonton, Alberta, Canada. July 2002;32–41.

613. Tang L, Liu H, Zhang J, Nazeri Z. Community evolution in dynamic multi-mode networks. In: Proc 2008 ACM SIGKDD Int Conf Knowledge Discovery and Data Mining (KDD’08). Las Vegas, NV. Aug. 2008;677–685.

614. Toivonen H. Sampling large databases for association rules. In: Proc 1996 Int Conf Very Large Data Bases (VLDB’96). Bombay, India. Sept. 1996;134–145.

615. Towell GG, Shavlik JW. Extracting refined rules from knowledge-based neural networks. Machine Learning. Oct. 1993;13:71–101.

616. Tan PN, Steinbach M, Kumar V. Introduction to Data Mining Boston: Addison-Wesley; 2005.

617. Tanay A, Sharan R, Shamir R. Biclustering algorithms: A survey. In: Aluru S, ed. Handbook of Computational Molecular Biology. London: Chapman & Hall; 2004;26:1–26:17.

618. Tufte ER. The Visual Display of Quantitative Information Graphics Press 1983.

619. Tufte ER. Envisioning Information Graphics Press 1990.

620. Tufte ER. Visual Explanations: Images and Quantities, Evidence and Narrative Graphics Press 1997.

621. Tufte ER. The Visual Display of Quantitative Information 2nd ed. Graphics Press 2001.

622. Tao Y, Xiao X, Zhou S. Mining distance-based outliers from large databases in any metric space. In: Proc 2006 ACM SIGKDD Int Conf Knowledge Discovery in Databases (KDD’06). Philadelphia, PA. Aug. 2006;394–403.

623. Utgoff PE, Berkman NC, Clouse JA. Decision tree induction based on efficient tree restructuring. Machine Learning. 1997;29:5–44.

624. Uthurusamy R, Fayyad UM, Spangler S. Learning useful rules from inconclusive data. In: Piatetsky-Shapiro G, Frawley WJ, eds. Knowledge Discovery in Databases. AAAI/MIT Press 1991;141–157.

625. Utgoff PE. An incremental ID3. In: Proc Fifth Int Conf Machine Learning (ICML’88). San Mateo, CA. 1988;107–120.

626. Valduriez P. Join indices. ACM Trans Database Systems. 1987;12:218–246.

627. Vapnik VN. The Nature of Statistical Learning Theory Springer Verlag 1995.

628. Vapnik VN. Statistical Learning Theory John Wiley & Sons 1998.

629. Vapnik VN, Chervonenkis AY. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and Its Applications. 1971;16:264–280.

630. Vaidya J, Clifton C. Privacy-preserving k-means clustering over vertically partitioned data. In: Proc 2003 ACM SIGKDD Int Conf Knowledge Discovery and Data Mining (KDD’03). Washington, DC. Aug 2003.

631. Vuk M, Curk T. ROC curve, lift chart and calibration plot. Metodološki zvezki. 2006;3:89–108.

632. Vaidya J, Clifton CW, Zhu YM. Privacy Preserving Data Mining New York: Springer; 2010.

633. Vlachos M, Gunopulos D, Kollios G. Discovering similar multidimensional trajectories. In: Proc 2002 Int Conf Data Engineering (ICDE’02). San Fransisco, CA. Apr. 2002;673–684.

634. Veloso A, Meira W, Zaki M. Lazy associative classificaiton. In: Proc 2006 Int Conf Data Mining (ICDM’06). Hong Kong, China. 2006;645–654.

635. van Rijsbergen CJ. Information Retrieval Butterworth 1990.

636. Vitter JS, Wang M, Iyer BR. Data cube approximation and histograms via wavelets. In: Proc 1998 Int Conf Information and Knowledge Management (CIKM’98). Washington, DC. Nov. 1998;96–104.

637. Waterman MS. Introduction to Computational Biology: Maps, Sequences, and Genomes (Interdisciplinary Statistics) CRC Press 1995.

638. Watts DJ. Six Degrees: The Science of a Connected Age W. W. Norton & Company 2003.

639. Westphal C, Blaxton T. Data Mining Solutions: Methods and Tools for Solving Real-World Problems John Wiley & Sons 1998.

640. Wu T, Chen Y, Han J. Re-examination of interestingness measures in pattern mining: A unified framework. Data Mining and Knowledge Discovery. 2010;21(3):371–397.

641. Wagstaff K, Cardie C, Rogers S, Schrödl S. Constrained k-means clustering with background knowledge. In: Proc 2001 Int Conf Machine Learning (ICML’01). Williamstown, MA. June 2001;577–584.

642. Weiss GM. Mining with rarity: A unifying framework. SIGKDD Explorations. 2004;6:7–19.

643. Wasserman S, Faust K. Social Network Analysis: Methods and Applications Cambridge University Press 1994.

644. Witten IH, Frank E. Data Mining: Practical Machine Learning Tools and Techniques 2nd ed. Morgan Kaufmann 2005.

645. Witten IH, Frank E, Hall MA. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations 3rd ed. Boston: Morgan Kaufmann; 2011.

646. Wang H, Fan W, Yu PS, Han J. Mining concept-drifting data streams using ensemble classifiers. In: Proc 2003 ACM SIGKDD Int Conf Knowledge Discovery and Data Mining (KDD’03). Washington, DC. Aug. 2003;226–235.

647. Wang K, He Y, Han J. Mining frequent itemsets using support constraints. In: Proc 2000 Int Conf Very Large Data Bases (VLDB’00). Cairo, Egypt. Sept. 2000;43–52.

648. Wang C, Han J, Jia Y, et al. Mining advisor-advisee relationships from research publication networks. In: Proc 2010 ACM SIGKDD Conf Knowledge Discovery and Data Mining (KDD’10). Washington, DC. July 2010.

649. Wang J, Han J, Lu Y, Tzvetkov P. TFP: An efficient algorithm for mining top-k frequent closed itemsets. IEEE Trans Knowledge and Data Engineering. 2005;17:652–664.

650. Wang J, Han J, Pei J. CLOSET+: Searching for the best strategies for mining frequent closed itemsets. In: Proc 2003 ACM SIGKDD Int Conf Knowledge Discovery and Data Mining (KDD’03). Washington, DC. Aug. 2003;236–245.

651. Weiss SM, Indurkhya N. Predictive Data Mining Morgan Kaufmann 1998.

652. Widom J. Research problems in data warehousing. In: Proc 4th Int Conf Information and Knowledge Management. Baltimore, MD. Nov. 1995;25–30.

653. Weiss S, Indurkhya N, Zhang T, Damerau F. Text Mining: Predictive Methods for Analyzing Unstructured Information New York: Springer; 2004.

654. Weiss SM, Kulikowski CA. Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems Morgan Kaufmann 1991.

655. Wang J, Karypis G. HARMONY: Efficiently mining the best rules for classification. In: Proc 2005 SIAM Conf Data Mining (SDM’05). Newport Beach, CA. Apr. 2005;205–216.

656. Wang W, Lu H, Feng J, Yu JX. Condensed cube: An effective approach to reducing data cube size. In: Proc 2002 Int Conf Data Engineering (ICDE’02). San Fransisco, CA. Apr. 2002;155–165.

657. Widrow B, Rumelhart DE, Lehr MA. Neural networks: Applications in industry, business and science. Communications of the ACM. 1994;37:93–105.

658. Wang R, Storey V, Firth C. A framework for analysis of data quality research. IEEE Trans Knowledge and Data Engineering. 1995;7:623–640.

659. Wu CFJ. On the convergence properties of the EM algorithm. Ann Statistics. 1983;11:95–103.

660. Wand Y, Wang R. Anchoring data quality dimensions in ontological foundations. Communications of the ACM. 1996;39:86–95.

661. Wang H, Wang W, Yang J, Yu PS. Clustering by pattern similarity in large data sets. In: Proc 2002 ACM-SIGMOD Int Conf Management of Data (SIGMOD’02). Madison, WI. June 2002;418–427.

662. Wu T, Xin D, Han J. ARCube: Supporting ranking aggregate queries in partially materialized data cubes. In: Proc 2008 ACM SIGMOD Int Conf Management of Data (SIGMOD’08). Vancouver, British Columbia, Canada. June 2008;79–92.

663. Wu T, Xin D, Mei Q, Han J. Promotion analysis in multi-dimensional space. Proc 2009 Int Conf Very Large Data Bases (VLDB’09). Aug. 2009;2(1):109–120.

664. Wang W, Yang J, Muntz R. STING: A statistical information grid approach to spatial data mining. In: Proc 1997 Int Conf Very Large Data Bases (VLDB’97). Athens, Greece. Aug. 1997;186–195.

665. Xin D, Cheng H, Yan X, Han J. Extracting redundancy-aware top-k patterns. In: Proc 2006 ACM SIGKDD Int Conf Knowledge Discovery in Databases (KDD’06). Philadelphia, PA. Aug. 2006;444–453.

666. Xin D, Han J, Cheng H, Li X. Answering top-k queries with multi-dimensional selections: The ranking cube approach. In: Proc 2006 Int Conf Very Large Data Bases (VLDB’06). Seoul, Korea. Sept. 2006;463–475.

667. Xin D, Han J, Li X, Wah BW. Star-cubing: Computing iceberg cubes by top-down and bottom-up integration. In: Proc 2003 Int Conf Very Large Data Bases (VLDB’03). Berlin, Germany. Sept. 2003;476–487.

668. Xin D, Han J, Shao Z, Liu H. C-cubing: Efficient computation of closed cubes by aggregation-based checking. In: Proc 2006 Int Conf Data Engineering (ICDE’06). Atlanta, GA. Apr. 2006;4.

669. Xin D, Han J, Yan X, Cheng H. Mining compressed frequent-pattern sets. In: Proc 2005 Int Conf Very Large Data Bases (VLDB’05). Trondheim, Norway. Aug. 2005;709–720.

670. Xiang Y, Olesen KG, Jensen FV. Practical issues in modeling large diagnostic systems with multiply sectioned Bayesian networks. Intl J Pattern Recognition and Artificial Intelligence (IJPRAI). 2000;14:59–71.

671. Xing Z, Pei J, Keogh E. A brief survey on sequence classification. SIGKDD Explorations. 2010;12:40–48.

672. Xiong H, Shekhar S, Huang Y, Kumar V, Ma X, Yoo JS. A framework for discovering co-location patterns in data sets with extended spatial objects. In: Proc 2004 SIAM Int Conf Data Mining (SDM’04). Lake Buena Vista, FL. Apr. 2004.

673. Xu X, Yuruk N, Feng Z, Schweiger TAJ. SCAN: A structural clustering algorithm for networks. In: Proc 2007 ACM SIGKDD Int Conf Knowledge Discovery in Databases (KDD’07). San Jose, CA. Aug. 2007;824–833.

674. Xu T, Zhang ZM, Yu PS, Long B. Evolutionary clustering by hierarchical Dirichlet process with hidden Markov state. In: Proc 2008 Int Conf Data Mining (ICDM’08). Pisa, Italy. Dec. 2008;658–667.

675. Ye N, Chen Q. An anomaly detection technique based on a chi-square statistic for detecting intrusions into information systems. Quality and Reliability Engineering International. 2001;17:105–112.

676. Yan X, Cheng H, Han J, Xin D. Summarizing itemset patterns: A profile-based approach. In: Proc 2005 ACM SIGKDD Int Conf Knowledge Discovery in Databases (KDD’05). Chicago, IL. Aug. 2005;314–323.

677. Yang C, Fayyad U, Bradley PS. Efficient discovery of error-tolerant frequent itemsets in high dimensions. In: Proc 2001 ACM SIGKDD Int Conf Knowledge Discovery in Databases (KDD’01). San Fransisco, CA. Aug. 2001;194–203.

678. Yoda K, Fukuda T, Morimoto Y, Morishita S, Tokuyama T. Computing optimized rectilinear regions for association rules. In: Proc 1997 Int Conf Knowledge Discovery and Data Mining (KDD’97). Newport Beach, CA. Aug. 1997;96–103.

679. Yan X, Han J. gSpan: Graph-based substructure pattern mining. In: Proc 2002 Int Conf Data Mining (ICDM’02). Maebashi, Japan. Dec. 2002;721–724.

680. Yan X, Han J. CloseGraph: Mining closed frequent graph patterns. In: Proc 2003 ACM SIGKDD Int Conf Knowledge Discovery and Data Mining (KDD’03). Washington, DC. Aug. 2003;286–295.

681. Yin X, Han J. CPAR: Classification based on predictive association rules. In: Proc 2003 SIAM Int Conf Data Mining (SDM’03). San Fransisco, CA. May 2003;331–335.

682. Yan X, Han J, Afshar R. CloSpan: Mining closed sequential patterns in large datasets. In: Proc 2003 SIAM Int Conf Data Mining (SDM’03). San Fransisco, CA. May 2003;166–177.

683. Yu PS, Han J, Faloutsos C. Link Mining: Models, Algorithms and Applications New York: Springer; 2010.

684. Yin X, Han J, Yu PS. Cross-relational clustering with user’s guidance. In: Proc 2005 ACM SIGKDD Int Conf Knowledge Discovery in Databases (KDD’05). Chicago, IL. Aug. 2005;344–353.

685. Yin X, Han J, Yu PS. Object distinction: Distinguishing objects with identical names by link analysis. In: Proc 2007 Int Conf Data Engineering (ICDE’07). Istanbul, Turkey. Apr. 2007.

686. Yin X, Han J, Yu PS. Truth discovery with multiple conflicting information providers on the Web. IEEE Trans Knowledge and Data Engineering. 2008;20:796–808.

687. Yin X, Han J, Yang J, Yu PS. CrossMine: Efficient classification across multiple database relations. In: Proc 2004 Int Conf Data Engineering (ICDE’04). Boston, MA. Mar. 2004;399–410.

688. Ye L, Keogh E. Time series shapelets: A new primitive for data mining. In: Proc 2009 ACM SIGKDD Int Conf Knowledge Discovery and Data Mining (KDD’09). Paris, France. June 2009;947–956.

689. Yuan J, Wu Y, Yang M. Discovery of collocation patterns: From visual words to visual phrases. In: Proc IEEE Conf Computer Vision and Pattern Recognition (CVPR’07). Minneapolis, MN. June 2007;1–8.

690. Yu H, Yang J, Han J. Classifying large data sets using SVM with hierarchical clusters. In: Proc 2003 ACM SIGKDD Int Conf Knowledge Discovery and Data Mining (KDD’03). Washington, DC. Aug. 2003;306–315.

691. Yan X, Yu PS, Han J. Graph indexing based on discriminative frequent structure analysis. ACM Trans Database Systems. 2005;30:960–993.

692. Yager RR, Zadeh LA. Fuzzy Sets, Neural Networks and Soft Computing Van Nostrand Reinhold 1994.

693. Yan X, Zhu F, Yu PS, Han J. Feature-based substructure similarity search. ACM Trans Database Systems. 2006;31:1418–1453.

694. Zadeh LA. Fuzzy sets. Information and Control. 1965;8:338–353.

695. Zadeh L. Commonsense knowledge representation based on fuzzy logic. Computer. 1983;16:61–65.

696. Zaki MJ. Scalable algorithms for association mining. IEEE Trans Knowledge and Data Engineering. 2000;12:372–390.

697. Zaki M. SPADE: An efficient algorithm for mining frequent sequences. Machine Learning. 2001;40:31–60.

698. Zhao Y, Deshpande PM, Naughton JF. An array-based algorithm for simultaneous multidimensional aggregates. In: Proc 1997 ACM-SIGMOD Int Conf Management of Data (SIGMOD’97). Tucson, AZ. May 1997;159–170.

699. Zaki MJ, Hsiao CJ. CHARM: An efficient algorithm for closed itemset mining. In: Proc 2002 SIAM Int Conf Data Mining (SDM’02). Arlington, VA. Apr. 2002;457–473.

700. Zhai C. Statistical Language Models for Information Retrieval Morgan and Claypool 2008.

701. Zaïane OR, Han J, Li ZN, Chiang JY, Chee S. MultiMedia-Miner: A system prototype for multimedia data mining. In: Proc 1998 ACM-SIGMOD Int Conf Management of Data (SIGMOD’98). Seattle, WA. June 1998;581–583.

702. Zhu X. Semi-supervised learning literature survey. In: Computer Sciences Technical Report 1530. University of Wisconsin–Madison 2005.

703. Zaïane OR, Han J, Zhu H. Mining recurrent items in multimedia with progressive resolution refinement. In: Proc 2000 Int Conf Data Engineering (ICDE’00). San Diego, CA. Feb. 2000;461–470.

704. Ziarko W. The discovery, analysis, and representation of data dependencies in databases. In: Piatetsky-Shapiro G, Frawley WJ, eds. Knowledge Discovery in Databases. AAAI Press 1991;195–209.

705. Zhou Z-H, Liu X-Y. Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans Knowledge and Data Engineering. 2006;18:63–77.

706. Zaki MJ, Parthasarathy S, Ogihara M, Li W. Parallel algorithm for discovery of association rules. Data Mining and Knowledge Discovery. 1997;1:343–374.

707. Zhang T, Ramakrishnan R, Livny M. BIRCH: An efficient data clustering method for very large databases. In: Proc 1996 ACM-SIGMOD Int Conf Management of Data (SIGMOD’96). Montreal, Quebec, Canada. June 1996;103–114.

708. Zapkowicz N, Stephen S. The class imbalance program: A systematic study. Intelligence Data Analysis. 2002;6:429–450.

709. Zhu F, Yan X, Han J, Yu PS, Cheng H. Mining colossal frequent patterns by core pattern fusion. In: Proc 2007 Int Conf Data Engineering (ICDE’07). Istanbul, Turkey. Apr. 2007;706–715.

710. Zhu F, Yan X, Han J, Yu PS. gPrune: A constraint pushing framework for graph pattern mining. In: Proc 2007 Pacific-Asia Conf Knowledge Discovery and Data Mining (PAKDD’07). Nanjing, China. May 2007;388–400.

711. Zhang Z, Zhang R. Multimedia Data Mining: A Systematic Introduction to Concepts and Theory Chapman & Hall 2009.

712. Zhang D, Zhai C, Han J. Topic cube: Topic modeling for OLAP on multidimensional text databases. In: Proc 2009 SIAM Int Conf Data Mining (SDM’09). Sparks, NV. Apr. 2009;1123–1134.

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