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.
Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to
cluster analysis.
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,
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Chakrabarti, S., Dom, B. E., Kumar, S. R., Raghavan,
P., Rajagopalan, S., Tomkins,
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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).
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.
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.
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