Bibliography

[ACO 07] ACOMB K., BLOOM J., DAYANIDHI K., HUNTER P., KROGH P., LEVIN E., PIERACCINI R., “Technical Support Dialog Systems: Issues, Problems, and Solutions”, Proceedings of the Workshop on Bridging the Gap: Academic and Industrial Research in Dialog Technologies, Rochester, USA, 2007.

[ALD 64] ALDENDERFER M., BLASHFIELD R., Cluster Analysis, Sage, Newbury Park, CA, 1964.

[ARI 06] ARIFIN A., ASANO A., “Image segmentation by histogram thresholding using hierarchical cluster analysis”, Pattern Recognition Letters, vol. 27 no. 13, p. 1515-1521, 2006.

[ART 06] ARTHUR D., VASSILVITSKII S., “How slow is the k-mean method”, Proceedings the 22nd Annual ACM Symposium on Computational Geometry (SOCG 06), Sedona, Arizona, 2006.

[BAG 06] BAGIROV A. M., MARDANEH K., “Modified global k-means algorithm for clustering in gene expression data sets”, Proceedings of the 2006 Workshop on Intelligent Systems for Bioinformatics (WISB ’06), Darlinghurst, Australia, p. 23-28, 2006.

[BAR 00] BARDIA S. H.-P., HAR-PELED S., SADRI B., On Lloyd’s k-means Method, 2000.

[BEA 97] BEAULIEU M. M., GATFORD M., HUANG X., ROBERTSON S. E., WALKER S., WILLIAMS P., “Okapi at TREC-5”, Fifth Text Retrieval Conference (T-REC), MD, USA, NIST Special publication, 1997.

[BEL 98] BELZEK J. C., PAL N. R., “Some new indexes of cluster validity”, IEEE Transactions on Systems, Man and Cybernetics, vol. 28, p. 301-315, 1998.

[BLA 55] BLASHFIELD R. K., “The growth of cluster analysis: Tryon, Ward, and Johnson”, Multi-variate Behavioral Research, vol. 15 no. 4, p. 439-458, 1955.

[BLA 07] BLANCHARD A., “Understanding and customizing stopword lists for enhanced patent mapping”, World Patent Information, vol. 29 no. 4, p. 308-316, 2007.

[BLU 01] BLUM A., CHAWLA S., “Learning from labeled and unlabeled data using graph mincuts”, ICML ’01: Proceedings of the Eighteenth International Conference on Machine Learning, San Francisco, CA, p. 19-26, 2001.

[BOL 99] BOLEY D., GINI M., GROSS R., HAN E.-H., KARYPIS G., KUMAR V., MOBASHER B., MOORE J., HASTINGS K., “Partitioning-based clustering for web document categorization”, Decision Support System, vol. 27 no. 3, p. 329-341, Elsevier Science Publishers B.V., 1999.

[BRO 99a] BROWN P. O . , BOTSTEIN D., “Exploring the new world of the genome with DNA microarrays”, Nature Genetics, vol. 21 no. 1, p. 33-37, Nature Publishing Group, 1999.

[BRO 99b] BROWN P. O., BOTSTEIN D., “Nature genetics”, Exploring the New World of the Genome with DNA Microarrays, Nature Publishing Group, vol. 21 no. 1, p. 33-37, January 1999.

[BUR 98] BURGES C. J. C., “A tutorial on support vector machines for pattern recognition”, Data Mining and Knowledge Discovery, vol. 2, p. 121-167, 1998.

[BUZ 80] BUZO A., GRAY A. H., GRAY R. M., MARKEL J. D., “Speech coding based upon vector quantization”, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 28 no. 5, p. 562-574, 1980.

[CAR 02] CARSON C., BELONGIE S., GREENSPAN H., MALIK J., “Blobworld: image segmentation using expectation — maximization and its application to image querying”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, p. 1026-1038, 2002.

[CAS 95] CASTELLI V., C OVER T. M., “On the exponential value of labeled samples”, Pattern Recognition Letters, vol. 16 no. 1, p. 105-111, 1995.

[CHE 00] CHENG Y., CHURCH G. M., “Biclustering of expression data”, Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, AAAI Press, La Jolla, California, USA, p. 93-103, 2000.

[CHE 06] CHENG-YUAN TANG Y.-L. W., LEE Y.-C., “Cluster and clustering algorithm validity in image retrieval”, IEEE International Conference on Systems, Man and Cybernetics, vol. 2006, p. 3318-3323, 2006.

[CHE 09] JUN CHEN C., ZHAO ZHAN Y., JUN WEN C., “Hierarchical face recognition based on SVDD and SVM”, ESIAT (2), IEEE Computer Society, p. 692-695, 2009.

[CHU 99] CHU-CARROLL J., CARPENTER B., “Vector-based natural language call routing”, Computational Linguistics, vol. 25, p. 361-388, 1999.

[CHU 02] CHURCHILL G. A., “Fundamentals of experimental design for cDNA microarrays”, Nature Genetics, vol. 32 Suppl., p. 490-495, 2002.

[CLA 95] CLARKE L.P., VELTHUIZEN R.P., CAMACHO M.A., HEINE J.J., VAIDYANATHAN M., HALL L.O., THATCHER R.W., SILBIGER M.L., “MRI segmentation: methods and applications”, Magnetic Resonance Imaging, vol. 13 no. 3, p. 343-368, 1995.

[CLE 04a] CLEUZIOU G., MARTIN L., VRAIN C., “PoBOC: un algorithme de ‘soft-clustering’. Applications a l’apprentissage de regles et au traitement de donnees textuelles”, vol. RNTI-E-2 of Revue des Nouvelles Technologies de l’Information, Cepadues-Editions, p. 217-228, 2004.

[CLE 04b] CLEUZIOU G., MARTIN L., VRAIN C., “PoBOC: an overlapping clustering algorithm, application to rule-based classification and textual data”, Proceedings of ECAI, Valencia, Spain, p. 440-444, 2004.

[COT 00] COTTRELL M., HAMMER B., HASENFUSS E., VILLMANN T., “Batch neural gas”, WSOM 2005. Fifth International Wokshop on self organising maps, Paris, France, 2000.

[COX 61] COX D., “Tests of separate families of hypotheses”, Proceedigs of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, California, USA, p. 105-123, 1961.

[COX 62] COX D., “Further results on tests of separate families of hypotheses”, Journal of the Royal Statistical Society, vol. 24, p. 406-423, 1962.

[CUT 92] CUTTING D. R., PEDERSEN J. O., KARGER D., TUKEY J. W., “Scatter/gather: a cluster-based approach to browsing large document collections”, Proceedings of the Fifteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, p. 318-329, 1992.

[DAR 02] DARA R., STACEY D., “Clustering unlabeled data with SOMs improves classification of labeled real-world data”, Proceedings of the Ninth International Conference on Fuzzy Systems, Honolulu Hawai, USA, 2002.

[DAV 79] DAVIES D., BOULDIN D., “A cluster separation measure”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, p. 224-227, 1979.

[DAV 91] DAVIS L. D., MITCHELL M., “Handbook of Genetic Algorithms”, Van Nostrand Reinhold, 1991.

[DEE 90] DEERWESTER S., DUMAIS S. T., FURNAS G. W., LANDAUER T. K . , H ARSHMAN R., “Indexing by latent semantic analysis”, Journal of the American Society for Information Science, vol. 41 no. 6, p. 391-407, 1990.

[DEM 77] DEMPSTER A. P., LAIRD N. M., RUBIN D. B., “Maximum likelihood from incomplete data via the EM algorithm”, Journal of the Royal Statistical Society, Series B, vol. 39 no. 1, p. 1-38, 1977.

[DEM 99] DEMIRIZ A., BENNETT K., EMBRECHTS M. J., “Semi-supervised clustering using genetic algorithms”, Artificial Neural Networks in Engineering (ANNIE-99), p. 809-814, 1999.

[DEP 00] DEPARTMENT Y. K., KO Y., SEO J., “Automatic text categorization by unsupervised learning”, Proceedings of COLING-2000, p. 453-459, 2000.

[DES 00] DESIGN I. T., GABRYS B., PETRAKIEVA L., “Combining labelled and unlabelled data”, International Journal on Approximate Reasoning, vol. 35, p. 251-273, 2004.

[DES 09] DESHMUKH K. S., “Color image segmentation using fuzzy c-means clustering”, IICAI: International Indian Conference on Artificial Intelligence, p. 1802-1813, 2009.

[DRA 09] DRAGUT E., FANG F. , SISTLA P. , YU C., “Stop word and related problems in web interface Integration”, Proceedings of the VLDB Endowment, vol. 2 no. 1, p. 349-360, 2009.

[DUN 74] DUNN J., “Well separated clusters and optimal fuzzy partitions”, Journal of Cybernetics, vol. 4, p. 95-104, 1974.

[EIS 98] EISEN M. B., SPELLMAN P. T., BROWN P. O., BOTSTEIN D., “Cluster analysis and display of genome-wide expression patterns”, Proceedings of the National Academy of Sciences of the United States of America, vol. 95 no. 25, p. 14863-14868, 1998.

[EQU 89] EQUITZ W. H., “A new vector quantization clustering algorithm”, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 37 no. 10, p. 1568-1575, 1989.

[EST 96] ESTER M., KRIEGEL H.-P., SANDER J., XU X., “A density-based algorithm for discovering clusters in large spatial databases with noise”, Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, KDD-96, p. 226-231, August 1996.

[FEN 09] FENG W. , XIE L., LIU Z.-Q., “Multicue graph mincut for image segmentation”, ACCV (2), vol. 5995 of Lecture Notes in Computer Science, Springer, p. 707-717, 2009.

[FIS 58] FISHER, W. D., “On Grouping for Maximum Homogeneity”, Journal of the American Statistical Association, vol. 53, p. 789-798, 1958.

[FOR 03] FORMAN G., “An extensive empirical study of feature selection metrics for text classification”, Journal of Machine Learning Research, vol. 3, p. 1289-1305, MIT Press, 2003.

[FRE 81] FREEDMAN D., DIACONIS P., “On the histogram as a density estimator: L2 theory”, Probability Theory and Related Fields, vol. 57 no. 4, p. 453-476, 1981.

[FRI 95] FRITZKE B., “A growing neural gas network learns topologies”, Advances in Neural Information Processing Systems 7, MIT Press, p. 625-632, 1995.

[FRO 00] FROHNE, H., Sample quantiles, Research Project, 2000.

[FUH 89] FUHR N., “Models for retrieval with probabilistic indexing”, Information Processing and Management, p. 55-72, 1989.

[FYF 06] FYFE C., “The topographic neural gas”, Proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL06, Burgos, Spain, p. 241-249, 2006.

[GER 91] GERSHO A., GRAY R. M., Vector Quantization and Signal Compression, Kluwer Academic Press, 1991.

[GET 00] GETZ G., LEVINE E., DOMANY E., “Coupled two-way clustering analysis of gene microarray data”, Proceedings of the National Academy of Sciences of the United States of America, vol. 97, p. 12079-12084, 2000.

[GOL 89] GOLDBERG D. E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Professional, 1989.

[GOL 03] GOLIN M. J., Bipartite matching and the Hungarian method, http://www.cs.ust.hk/~golin/COMP572/Notes/Matching.pdf, 2003.

[GRA 05] GRAHAM J. R., MMPI-2: Assessing Personality and Psychopathology, Oxford University Press, 2005.

[GUY 03] GUYON I., “An introduction to variable and feature selection”, Journal of Machine Learning Research, vol. 3, p. 1157-1182, 2003.

[HAL 00] HALKIDI M., VAZIRGIANNIS M., BATISTAKIS Y., “Quality scheme assessment in the clustering process”, PKDD ’00: Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, London, Springer-Verlag, p. 265-276, 2000.

[HAL 02a] HALKIDI M., BATISTAKIS Y., VAZIRGIANNIS M., “Cluster validity methods: part I”, ACM SIGMOD Record, vol. 31, p. 2002, 2002.

[HAL 02b] HALKIDI M., BATISTAKIS Y., VAZIRGIANNIS M., “Clustering validity checking methods: part II”, SIGMOD Record, vol. 31 no. 3, p. 19-27, ACM, 2002.

[HAR 75] HARTIGAN J., Clustering Algorithms, Wiley, New York, NY, 1975.

[HAR 05] HARPELED S., SADRI B., “How fast is the k-means method”, Algorithmica, vol. 41, p. 185-202, 2005.

[HAS 00] HASTIE T. , TIBSHIRANI R., EISEN M., ALIZADEH A., LEVY R., STAUDT L., CHAN W., BOTSTEIN D., BROWN P., “‘Gene shaving’ as a method for identifying distinct sets of genes with similar expression patterns”, Genome Biology, vol. 1 no. 2, p. 1-21, 2000.

[HAS 09] HASTIE T. , TIBSHIRANI R., FRIEDMAN J., Hierarchical Clustering. The Elements of Statistical Learning, Springer, New York, NY, 2009.

[HAV 08] HAVENS T., BEZDEK J., KELLER J., POPESCU M., “Dunn’s cluster validity index as a contrast measure of VAT images”, International Conference on Pattern Recognition (ICPR), Tampa Convention Center, Tampa, Florida, USA, p. 1-4, December 2008.

[HEA 96] HEARST M. A., PEDERSEN J. O., “Reexamining the cluster hypothesis: scatter/gather on retrieval results”, Proceedings of the 19th Annual International ACM/SIGIR Conference, Zurich, Switzerland, p. 76-84, 1996.

[JAI 99] JAIN A. K., MURTY M. N., FLYNN P. J., “Data clustering: a review”, ACM Computing Surveys, vol. 31 no. 3, p. 264, 1999.

[JAI 04] JAIN A. K., “Lanscape of clustering algorithms”, Proceedings of the 17th International Conference on Pattern Recognition (ICPR), 2004.

[JOA 97] JOACHIMS T., INFORMATIK F., INFORMATIK F., INFORMATIK F. , INFORMATIK F. , V III L., “Text categorization with support vector machines: learning with many relevant features”, Proceedings of the First European Conference on Machine Learning (ECML 98), p. 137-142, 1998.

[JOL 89] JOLION J.-M., ROSENFELD A., “Cluster detection in background noise”, Pattern Recognition, vol. 22 no. 5, p. 603-607, Elsevier Science, Inc., 1989.

[JON 07] JONES K. S., “Information retrieval and digital libraries: lessons of research”, Proceedings of the 2006 International Workshop on Research Issues in Digital Libraries (IWRIDL ’06), Kolkata, India, p. 1-7, 2007.

[KAR 98] KARYPIS G., KUMAR V., METIS: a software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices, University of Minnesota, USA, 1998.

[KLE 00] KLEIN S. T., “On the use of negation in Boolean IR queries”, Information Processing and Management, vol. 45, no. 2, p. 291-311, 2000.

[KOH 90] KOHONEN T., “The self-organizing map”, Proceedings of the IEEE, vol. 79 no. 9, 1990.

[KOH 01] KOHONEN T., SCHROEDER M. R., HUANG T. S., Eds., Self-Organizing Maps, Springer-Verlag New York, Inc., Secaucus, NJ, 2001.

[KRZ 85] KRZANOWSKI W. , LAI Y., “A criterion for determining the number of groups in a data set using sum of squares clustering”, Biometrics, vol. 44, p. 23-34, 1985.

[KUH 55] KUHN H. W., “The Hungarian method for the assignment problem”, Naval Research Logistics Quarterly, vol. 2, p. 83-97, 1955.

[LAN 98] LANDAUER T. K., FOLTZ P. W., LAHAM D., “An introduction to latent semantic analysis”, Discourse Processes, no. 25, p. 259-284, 1998.

[LAS 09] LASHKARI A. H., MAHDAVI F. , GHOMI V., “A Boolean model in information retrieval for search engines”, Information Management and Engineering, International Conference on ICIME 2009, Kuala Lumpur, Malaysia, p. 385-389, IEEE Computer Society, 2009.

[LEO 05] LEONARD KAUFMAN P. J. R . , Finding Groups in Data: An Introduction to Cluster Analysis, Wiley Series in Probability and Statistics, New York, NY, 2005.

[LI 98] LI Y. H . , JAIN A. K., “Classification of text documents”, The Computer Journal, vol. 41, p. 537-546, 1998.

[LIN 06] LIN Y.-M., WANG X., NG W., CHANG Q., YEUNG D., WANG X.-L., “Sphere classification for ambiguous data”, Machine Learning and Cybernetics, 2006 International Conference on ICMLC, Dalian, China, p. 2571-2574, 2006.

[LO 07] LO E. H. S., PICKERING M. R., FRATER M. R., ARNOLD J. F., “Image segmentation using invariant texture features from the double dyadic dual-tree complex wavelet transform”, Proceeding of ICASSP, IEEE, Honolulu, Hawai’i USA, p. 609-612, 2007.

[MAC 08] MACHLACHLAN G., KRISHNAN T. , The EM Algorithm and Extensions, Wiley Series in Probability and Statistics, 2008.

[MAE 04] MAEIREIZO B., LITMAN D., HWA R., “Co-training for predicting emotions with spoken dialogue data”, Proceedings of the ACL 2004 on Interactive Poster and Demonstration Sessions, Companion Volume to the proceeding of 42nd Annual Meeting of the Association for Computational Linguistics (ACL), July 2004, Barcelona, Spain.

[MAN 00] MANCAS M., GOSSELIN B., MACQ B., “Segmentation using a region-growing thresholding”, Image Processing: Algorithms and Systems, Proceedings of the SPIE, p. 388-398, 2000.

[MAN 08] MANNING C. D., RAGHAVAN P., SCHÜTZE H., Introduction to Information Retrieval, Cambridge University Press, 2008.

[MAR 60] MARON M. E., KUHNS J. L., “On relevance, probabilistic indexing and information retrieval”, Journal of the ACM, vol. 7 no. 3, p. 216-244, ACM, 1960.

[MIN 01] MINNEN G., CARROL J., PEARCE D., “Applied morphological processing of English”, Natural Language Engineering, vol. 7 no. 3, 2001.

[MUK 08] MUKHOPADHYAY A., BANDYOPADHYAY S., MAULIK U., “Combining multiobjective fuzzy clustering and probabilistic ANN classifier for unsupervised pattern classification: application to satellite image segmentation”, IEEE Congress on Evolutionary Computation, IEEE, p. 877-883, 2008.

[MUR 07] MURRAY G., RENALS S., “Towards online speech summarization”, Proceedings of the Interspeech ’07, Antwert, Belgium, 2007.

[MUR 08] MURRAY G., RENALS S., “Term-weighting for summarization of multi-party spoken dialogues”, Proceedings of the 4th International Conference on Machine Learning for Multimodal Interaction, Berlin, p. 156-167, 2008.

[NA 09] NA S.-H., NG H. T., “A 2-Poisson model for probabilistic coreference of named entities for improved text retrieval”, SIGIR ’09: Proceedings of the 32nd international ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, p. 275-282, 2009.

[NCI 06] NCI Cancer Microarray Data (Stanford University), 2006, http://genome-www.stanford.eud/nci60.

[NIG 99] NIGAM K., MCCALLUM A. K., THRUN S., MITCHELL T. , “Text classification from labeled and unlabeled documents using EM”, Machine Learning, May 2000, vol. 39, no. 2, p. 103-134, 1999.

[NIG 00] NIGAM K., MCCALLUM A. K., THRUN S., MITCHELL T. , “Text classification from labeled and unlabeled documents using EM”, International Journal of Machine Learning, vol. 39 no. 2-3, p. 103-134, 2000.

[OUY 09] OUYANG C.-S., CHOU C.-T., JHAN C.-F., HUANG J.-Y., “An improved approach for image segmentation based on color and local homogeneity features”, ICASSP, IEEE, Taipei, Taiwan, p. 1225-1228, 2009.

[PAR 07] PARK J.-A., KANG S. K., JEONG I., RASHEED W. , PARK S., AN Y., “Web based image retrieval system using HSI color indexes”, ICIC (3), vol. 2 of Communications in Computer and Information Science, Springer, p. 199-207, 2007.

[PIC 99] PICARD J., “Finding content-bearing terms using term similarities”, Proceedings of Ninth Conference of the European Chapter of the Association for Computational Linguistics, Bergen, Norway, p. 241-244, June 1999.

[POR 80] PORTER M. F., “An algorithm for suffix stripping”, Program, vol. 14 no. 3, p. 130-137, 1980.

[POR 96] R. PORTER, CANAGARAJAH N., “A robust automatic clustering scheme for image segmentation using wavelets”, IEEE Transactions on Image Processing, vol. 5 no. 4, p. 662-665, 1996.

[PRI 03] PRIEGO J. L. O., “A vector space model as a methodological approach to the triple helix dimensionality: a comparative study of Biology and Biomedicine Centres of two European National Research Councils from a webometric view”, Scientometrics, p. 429-443, 2003.

[RAB 93] RABINER L., JUANG B. H., Fundamentals of Speech Recognition, Prentice Hall, 1993.

[RAM 00] RAMOS J., “Using TF-IDF to determine word relevance in document queries”, http://www.cs.rutgers.edu/mlitmann/courses/ml03/ICML03/papers/ramos.pdf, 2000.

[RIJ 79] VAN RIJSBERGEN C. J., Information Retrieval, Butterworths, London, 1979.

[ROB 92] ROBERTSON S. E., WALKER S., HANCOCK-BEAULIEU M., GULL A., LAU M., “Okapi at TREC”, Text Retrieval Conference, p. 21-30, 1992.

[ROB 99] ROBERTSON S., WALKER S., BEAULIEU M., WILLETT P., “Okapi at TREC-7: automatic ad hoc, filtering, VLC and interactive track”, , Seventh Text Retrieval Conference (TREC-7) Gaithersburg, Maryland, USA, vol. 21, p. 253-264, 1999.

[ROB 00] ROBERTSON S. E., WALKER S., BEAULIEU M., “Experimentation as a way of life: Okapi at TREC”, Information Processing and Management, January 96, vol. 36, no. 1 p. 95-108, 2000.

[ROB 01] ROBERT TIBSHIRANI G. W., HASTIE T., “Estimating the number of clusters in a dataset via the gap statistic”, Journal of the Royal Statistical Society, vol. 63, p. 411-423, 2001.

[ROE 00] ROELLEKE T. , WANG J., “Binary independence retrieval model, probabilistic relational modelling, integration of database and information retrieval (DB+IR)”, Probabilistic Logical Modeling of the Binary Independence Retrieval Model. In Proceedings of the First International Conference of Information Retrieval (ICTIR 07) — Studies in Theory of Information Retrieval, 2000.

[ROS 00] ROSS D. T., SCHERF U., EISEN M. B., PEROU C. M., REES C., SPELLMAN P. , IYER V., JEFFREY S. S., DE RIJN M. V., WALTHAM M., PERGAMENSCHIKOV A., LEE J. C., LASHKARI D., SHALON D., MYERS T. G., WEINSTEIN J. N., BOTSTEIN D., BROWN1 P. O., “Systematic variation in gene expression pattenrns in human cancer cell lines”, Nature Genetics, vol. 24 no. 3, p. 227-235, 2000.

[ROU 87] ROUSSEEUW P., “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis”, Journal of Computational and Applied Mathematics, vol. 20, p. 53-65, 1987.

[SAL 71] SALTON G., The SMART Retrieval System — Experiments in Automatic Document Processing, Prentice-Hall, Inc., Upper Saddle River, NJ, 1971.

[SAL 75] SALTON G., WONG A., YANG C.-S., “A vector space model for automatic indexing”, Communications of the ACM, vol. 18 no. 11, p. 613-620, 1975.

[SAL 88] SALTON G., BUCKLEY C., “Term-weighting approaches in automatic text retrieval”, Information Processing and Management, vol. 4, no. 5, p. 513-524, 1988.

[SAN 98] SANDER J., ESTER M., KRIEGEL H.-P., XU X., “Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications”, Data Mining and Knowledge Discovery, vol. 2 no. 2, p. 169-194, 1998.

[SCH 94] SCHMID H., “Probabilistic part-of-speech tagging using decision trees”, Proceedings of the International Conference on New Methods in Language Processing, Umist, Manchester, UK, p. 44-49, 1994.

[SEE 01] SEEGER M., Learning with labeled and unlabeled data, Report, 2001.

[SER 80] SERFLING R., Approximation Theorems of Mathematical Statistics, John Wiley and Sons, 1980.

[SHE 96] SHEPPARD A. G., “The sequence of factor analysis and cluster analysis: differences in segmentation and dimensionality through the use of raw and factor scores”, Tourism Analysis, vol. 1, p. 49-57, 1996.

[SIN 96a] SINGHAL A., SALTON G., BUCKLEY C., “Length normalization in degraded text collections”, Proceedings of Fifth Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, Nevada, USA, p. 15-17, 1996.

[SIN 96b] SINGHAL A., SALTON G., MITRA M., BUCKLEY C., Document length normalization, Report, Ithaca, NY, 1996.

[SIN 09] SINGHAL A., BUCKLEY C., MITRA M., “Pivoted document length normalization”, Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, p. 21-29, 1996.

[SOK 63] SOKAL R., SNEATH P. , Principles of Numerical Taxonomy, Morgan Freeman, San Francisco, CA, 1963.

[STE 00] STEIN B., EISSEN S. M. Z., POTTHAST M., “Syntax versus semantics: analysis of enriched vector space models”, 2000.

[STO 00] Stop word list of the Smart Information Retrieval Project, 2000, http://jmlr.csail.mit.edu/papers/volume5/lewis04a/a11-smart-stop-list/english.stop.

[STR 02] STREHL A., GHOSH J., CARDIE C., “Cluster ensembles — a knowledge reuse framework for combining multiple partitions”, Journal of Machine Learning Research, vol. 3, p. 583-617, 2002.

[TAM 99] TAMAYO P., SLONIM D., MESIROV J., ZHU Q., KITAREEWAN S., DMITROVSKY E., LANDER E. S., GOLUB T. R . , “Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation”, Proceedings of the National Academy of Sciences of the United States of America, vol. 96 no. 6, p. 2907-2912, 1999.

[THE 09] THEWES R., “Introduction to electronic DNA microarrays”, EPFL Summer School on Nano-Bio-Sensing, Sommer School, Lausanne, Switzerland, 2009.

[TOU 00] TOUTANOVA K., MANNING C. D., “Enriching the knowledge sources used in a maximum entropy part-of-speech tagger”, Proceedings of the 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, Morristown, NJ, Association for Computational Linguistics, p. 63-70, 2000.

[TRE 05] TREECK B., Entwicklung und Evaluierung einer Java-Schnittstelle zur Clusteranalyse von Peer-to-Peer Netzwerken. Bachelorarbeit, Heinrich Heine University, Duesseldorf, 2005.

[TRY 55] TRYON R., “Identification of social areas by cluster analysis”, University of California Publications in Psychology, no. 8, p. 1-100, 1955.

[TRY 68] TRYON R., “Comparative cluster analysis of variables and individuals: Holzinger abilities and MMPI attributes”, Multivariate Behavioral Research, vol. 3, p. 115-144, 1968.

[TRY 70] TRYON R., BAILEY D., Cluster Analysis, McGraw Hill, New York, NY, 1970.

[VES 00] VESANTO J., ALHONIEMI E., “Clustering of the self-organizing map”, IEEE Transactions on Neural Networks, vol. 11 no. 3, p. 586-600, 2000.

[WAN 08] WANG Y., ZUO W. , PENG T. , HE F. , HU H., “Clustering web search results based on interactive suffix tree algorithm”, Convergence Information Technology, International Conference on, vol. 2, p. 851-857, IEEE Computer Society, 2008.

[WAR 63] WARD J., “Hierarchical grouping to optimize an objective function”, Journal of the American Statistical Association, vol. 58 no. 301, p. 236-244, 1963.

[WOR 98] WordNet An Electronic Lexical Database, Cambridge, MA, London, 1998.

[WU 09] WU J., CHEN J., XIONG H., XIE M., “External validation measures for K-means clustering: a data distribution perspective”, Expert Systems with Applications, vol. 36 no. 3, p. 6050-6061, 2009.

[WUL 97] WULFEKUHLER M. R., PUNCH W. F., “Finding salient features for personal web page categories”, Proceedings of 6th International World Wide Web Conference, p. 6-118, 1997.

[YAN 97] YANG Y., PEDERSEN J. O., “A comparative study on feature selection in text categorization”, Proceedings of ICML-97, 14th International Conference on Machine Learning, Nashville, TN, p. 412-420, 1997.

[YAN 09] YANG X., CLAUSI D. A., ICIP, IEEE, p. 1721-1724, 2009.

[YAR 95] YAROWSKY D., “Unsupervised word sense disambiguation rivaling supervised methods”, Proceedings of the 33rd Annual Meeting on Association for Computational Linguistics, 1995.

[YE 09] YE P. , WENG G., “Microarray image segmentation using region growing algorithm and mathematical morphology”, IAS, IEEE Computer Society, p. 373-376, 2009.

[ZAM 98] ZAMIR O., ETZIONI O., “Web document clustering: a feasibility demonstration”, Research and Development in Information Retrieval, p. 46-54, 1998.

[ZHU 06] ZHU X., “Semi-supervised learning literature survey”, http://pages.cs.wisc.edu/jerryzhu/pub/ssl_survey.pdf, 2006.