268 Applied Data Mining
[34] A. H. Lashkari, F. Mahdavi and V. Ghomi. A boolean model in information retrieval for
search engines, 2009.
[35] R. S. U. A. Lehwark, P. Visualization and clustering of tagged music data. data analysis.
Machine Learning and Applications, pp. 673–680, 2008.
[36] L. F. e. a. Lei X. F., Xie K. Q. An effi cient clustering algorithm based on local optimality
of k-means. Journal of Software, 19(7): 1683–1692, 2008.
[37] U. V. Luxburg. A tutorial on spectral clustering. Statistics and Computing, 17(4), 2007.
[38] P. D. Martin Leginus and V. Zemaitis. Improving tensor based recommenders with
clustering. In: The 20th International Conference on User Modeling, Adaptation, and
Personalization (UMAP’12 ), pp. 151–163. Springer-Verlag Berlin, Heidelberg.
[39] E. A. Matteo N. R., Peroni S. and Tamburini F. A parametric architecture for tags clustering
in folksonomic search engines, 2009.
[40] G. H. Max Chevalier, Antonina Dattolo and E. Pitassi. Information retrieval and
folksonomies together for recommender systems. Systems E-Commerce and Web
Technologies, volume 85 of Lecture Notes: Chapter 15, pp. 172–183.
[41] T. J. H. W. S. A. M. L. Miao, G. Extracting data records from the web using tag path
clustering. In Proceedings of the 18th International Conference on World Wide Web, pp.
981–990. ACM.
[42] H. F. Michael J. B. Technical comments comment on ”Clustering by passing messages
between data points”. Science, 319: 726c–727c, 2008.
[43] P. Mika. Ontologies are us: A unifi ed model of social networks and semantics. In Y. Gil,
E. Motta, V. R. Benjamins and M. A. Musen, editors, ISWC 2005, volume 3729 of LNCS,
Berlin Heidelberg. Springer-Verlag., pp. 522–536, 2005.
[44] M. Montaner, B. Lopez and J. L. de la Rosa. A taxonomy of recommender agents on the
internet. Artifi cial Intelligence Review, 19(4): 285?30, 2003.
[45] H. W. Ng T. and Raymond J. Clarans: A method for clustering objects for spatial data
mining. IEEE Transactions on Knowldge and Data Engineering, 14(9): 1003–1026, 2002.
[46] F. T. e. a. Nicola R. D. and Silvio P. Of mice and terms: Clustering algorithms on ambi-
guous terms in folksonomies. In The 2010 ACM symposium on Applied Computing SAC10,
pp. 844–848.
[47] B. F. P. D. Z. W. M. L. Rong Pan, Guandong Xu. Improving recommendations by the
clustering of tag neighbours. Journal of Convergence, Section C, 3(1), 2012.
[48] S. Sen, S. K. Lam, A. M. Rashid, D. Cosley, D. Frankowski, J. Osterhouse, F. M. Harper
and J. Riedl. tagging, communities, vocabulary, evolution, November 2006.
[49] D. Shakhnarovish and Indyk. Nearest-neighbor methods in learning and vision. The
MIT Press, 2005.
[50] A. Shepitsen, J. Gemmell, B. Mobasher and R. Burke. Personalized recommendation
in social tagging systems using hierarchical clustering. In RecSys?008: Proceedings of the
2008 ACM conference on Recommender systems, pp. 259–266, 2008.
[51] D. J. Su T. A deterministic method for initializing k-menas clustering, 2004.
[52] B. K. Teknomo. K-means clustering tutorial.
[53] K. Thearling. An introduction to data mining: Discovering hidden value in your data
warehouse.
[54] R. D. Validity and A. K. Jain. Studies in clustering methodologies. Pattern Recognition,
pp. 235–254, 1979.
[55] V. D. H. F. F. F. Van Dam, J. Searching and browsing tagspaces using the semantic
tag clustering search framework. In: S. Computing(ICSC), editor, 2010 IEEE Fourth
International Conference, pp. 436–439. IEEE.
[56] wiki/Recommender system. http://en.wikipedia.org/wiki/recommender system.
[57] M. -L. Zhang and Z. -H. Zhou. Ml-knn: A lazy learning approach to multi-label learning.
Pattern Recognition, 40(7): 2038–2048, 2007.
[58] Q. L. e. a. Zhou J. L., Nie X.J. Web clustering based on tag set similarity. Journal of
Computers, 6(1): 59–66, 2011.
[59] Y. Zong, G. Xu, P. Jin, Y. Zhang, E. Chen and R. Pan. APPECT: An Approximate Backbone-
Based Clustering Algorithm for Tags Advanced Data Mining and Applications, volume 7120
of Lecture Notes in Computer Science, pp. 175–189. Springer Berlin/Heidelberg, 2011.
[60] L. M. C. Zong Y. and Jiang H. Approximate backbone guided reduction clustering
algorithm. Journal of electronics and information technology, 31(2)(2953–2957), 2009.
[61] C. G. Zou P. and ZHou Z. H. Approximate backbone guided fast ant algorithm to qap.
Journal of Software, 16(10): 1691–1698, 2005.
[62] Xiaoyuan Su and Taghi M. Khoshgoftaar. A survey of collaborative fi ltering techniques.
Advances in Artifi cial Intelligence, Volume 2009, January 2009.
[63] Yehuda Koren. Factor in the neighbors: Scalable and accurate collaborative fi ltering.
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 4, Issue 1, January
2010.
[64] Zhu, T., Greiner, R. and Haubl, G. A fuzzy hybrid collaborative fi ltering technique for
web personalization. WWW2003, May, 2003, Budapest, Hungary.
[65] Claypool, M., Gokhale, A. and Miranda, T. Combining content-based and collaborative
fi lters in an online newspaper. ACM SIGIR Workshop on Recommender Systems.
[66] Dasari Siva Krishna and K. Rajani Devi. Improving Accumulated Recommendation
System A Comparative Study of Diversity Using Pagerank Algorithm Technique.
International Journal of Advanced Science and Technology.
[67] Sergey Brin, Lawrence Page. The anatomy of a large-scale hypertextual web search
engine. Computer Networks. Vol. 30, Issue 1-7, April 1, 1998.
[68] Kubatz, M., Gedikli, F., Jannach and D. LocalRank—Neighborhood-based, fast
computation of tag recommendations. 12th International Conference on Electronic Commerce
and Web Technologies - EC-Web 2011.
[69] Christopher D. Manning, Prabhakar Raghavan, Hinrich Schutze. Introduction to
Information Retrieval. Cambridge University Press.
[70] P. Tamayo, D. Slonim, J. Mesirov, Q. Zhu, S. Kitareewan, S. Dmitrovsky, E. Lander and
T. R. Golub. Interpreting patterns of gene expression with self-organizing maps: methods
and application to hematopoietic differentiation. National Academy of Sciences.
[71] Ulrike Von Luxburg. A Tutorial on Spectral Clustering. Statistics and Computing.
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