Clustering and Cluster Analysis

Books

Easlye, D. & Kleinberg, J. (2010) Networks, crowds, and markets : reasoning about a highly connected world (Part I: Graph Theory and Social Networks and Part IV: Information Networks and the World Wide Web). Cambridge: UK, Cambridge University Press. http://www.cs.cornell.edu/home/kleinber/networks-book/

Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (Eds.). (1996). Advances in knowledge discovery and data mining. AAAI/MIT Press.

Gan, G., Ma, C. & Wu, J. (2007). Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability). Philadelphia, PA: SIAM.

Jain, A. K., & Dubes, R. C. (1988). Algorithms for clustering data. New Jersey: Prentice Hall.

Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. New York: John Wiley.

Michie, D., Spiegelhalter, D. J., & Taylor, C. C. (1994). Machine learning, neural and statistical classification: Ellis Horwood.

 

Articles

Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1 - 2), 105.

Chakrabarti, S., Dom, B. E., Kumar, S. R., Raghavan, P., Rajagopalan, S., Tomkins, A., et al. (1999). Mining the web's link structure. Computer, 32(8), 60-67.

Cheeseman, P., & Stutz, J. (1996). Bayesian classification (autoclass): Theory and results. In Advances in knowledge discovery and data mining (pp. 153-180): American Association for Artificial Intelligence.

Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391-407.

Ferreira de Oliveira, M. C., & Levkowitz, H. (2003). From visual data exploration to visual data mining: A survey. Visualization and Computer Graphics, IEEE Transactions on, 9(3), 378. Handcock, M. S., Raftery, A. E., & Tantrum, J. M. (2007). Model based clustering for social networks. Journal of the Royal Statistical Society: Series A (Statistics in Society), 170(2), 301-354.

Handcock, M. S., Raftery, A. E., & Tantrum, J. M. (2007). Model based clustering for social networks. Journal of the Royal Statistical Society: Series A (Statistics in Society), 170(2), 301-354.

Jinwook, S., & Shneiderman, B. (2002). Interactively exploring hierarchical clustering results [gene identification]. Computer, 35(7), 80.

Kohonen, T. (1982). Analysis of a simple self-organizing process. Biological Cybernetics (Historical Archive), 44(2), 135.

Lagus, K., Honkela, T., Kaski, S., & Kohonen, T. (1999). Websom for textual data mining. Artificial Intelligence Review, 13(5 - 6), 345.

Milligan, G. W., & Hirtle, S. C. (2003). Clustering and classification methods. In J. Schinka & W. Velicer (Eds.), Comprehensive handbook of psychology (Vol. 2, pp. 165-186). New York: Wiley.

Ng, R. T., & Han, J. (2002). Clarans: A method for clustering objects for spatial data mining. Knowledge and Data Engineering, IEEE Transactions on, 14(5), 1003.

Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (1998). Learning to classify text from labeled and unlabeled documents, Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence. Madison, Wisconsin, United States: American Association for Artificial Intelligence.

Ramage, D., Heymann, P., Manning, C. D., & Garcia-Molina, H. (2009). Clustering the tagged web. WSDM '09: Proceedings of the Second ACM International Conference on Web Search and Data Mining. (pp. 54-63). Barcelona, Spain: ACM.

Rumelhart, D. E., & Zipser, D. (1985). Feature discovery by competitive learning. Cognitive Science, 9, 75-112.