Abstract
In this chapter, we provide an overview of the categorical data clustering problem. We first present different techniques for the general cluster analysis problem, and then study how these techniques specialize to the case of non-numerical (categorical) data. We also present measures and techniques developed specifically for this domain.
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Andritsos P, Tsaparas P, Miller RJ, Sevcik KC (2004) LIMBO: scalable clustering of categorical data. In: Proceedings of the 9th international conference on extending database technology (EDBT), Heraklion, 14–18 Mar 2004, pp 123–146
Barbarà D, Couto J, Li Y (2002) COOLCAT: an entropy-based algorithm for categorical clustering. In: Proceedings of the 11th international conference on information and knowledge management (CIKM), McLean, 4–9 Nov 2002, pp 582–589
Cover TM, Thomas JA (1991) Elements of information theory. Wiley, New York
Das G, Mannila H (2000) Context-based similarity measures for categorical databases. In: Proceedings of the 4th European conference on principles of data mining and knowledge discovery (PKDD), Lyon, 13–16 Sept 2000, pp 201–210
Fisher DH (1987) Knowledge acquisition via incremental conceptual clustering. Mach Learn 2: 139–172
Ganti V, Gehrke J, Ramakrishnan R (1999) CACTUS: clustering categorical data using summaries. In: Proceedings of the 5th international conference on knowledge discovery and data mining, (KDD), San Diego, 15–18 Aug 1999, pp 73–83
Gionis A, Mannila H, Tsaparas P (2007) Clustering aggregation. In: ACM transactions on knowledge discovery from data (TKDD), Mar 2007, vol 1, No 1. Association for Computing Machinery, New York
Gibson D, Kleinberg JM, Raghavan P (1998) Clustering categorical data: an approach based on dynamical systems. In: Proceedings of the 24rth international conference on very large data bases, (VLDB), New York, 24–27 Aug 1998, pp 311–322
Gluck M, Corter J (1985) Information, uncertainty, and the utility of categories. In: Proceedings of the 7th annual conference of the Cognitive Science Society (COGSCI), Irvine, pp 283–287
Guha S, Rastogi R, Shim K (1999) ROCK: a robust clustering algorithm for categorical atributes. In: Proceedings of the 15th international conference on data engineering, Sydney, 23–26 Mar 1999, pp 512–521
Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall, Englewood Cliffs
Jarke M, Lenzerini M, Vassiliou Y, Vassiliadis P (1999) Fundamentals of data warehouses. Springer-Verlag, Berlin/Heidelberg
Han J, Kamber M (2001) Data mining: concepts and techniques. Morgan Kaufmann, San Francisco
Huang Z (1998) Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min Knowl Discov 2(3):283–304
Zaki MJ, Peters M, Assent I, Seidl T (2005) CLICKS: an effective algorithm for mining subspace clusters in categorical datasets. In: Proceeding of the 11th international conference on knowledge discovery and data mining (KDD), Chicago, 21–24 Aug 2005, pp 736–742
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Andritsos, P., Tsaparas, P. (2016). Categorical Data Clustering. In: Sammut, C., Webb, G. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7502-7_35-1
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DOI: https://doi.org/10.1007/978-1-4899-7502-7_35-1
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Publisher Name: Springer, Boston, MA
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