Entropy-based criterion in categorical clustering

Tao Li; Sheng Ma; Mitsunori Ogihara

Entropy-based criterion in categorical clustering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Entropy-type measures for the heterogeneity of clusters have been used for a long time. This paper studies the entropy-based criterion in clustering categorical data. It first shows that the entropy-based criterion can be derived in the formal framework of probabilistic clustering models and establishes the connection between the criterion and the approach based on dissimilarity coefficients. An iterative Monte-Carlo procedure is then presented to search for the partitions minimizing the criterion. Experiments are conducted to show the effectiveness of the proposed procedure.

Original language	English (US)
Title of host publication	Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004
Editors	R. Greiner, D. Schuurmans
Pages	536-543
Number of pages	8
State	Published - Dec 1 2004
Externally published	Yes
Event	Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004 - Banff, Alta, Canada Duration: Jul 4 2004 → Jul 8 2004

Publication series

Name	Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004

Other

Other	Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004
Country/Territory	Canada
City	Banff, Alta
Period	7/4/04 → 7/8/04

ASJC Scopus subject areas

Engineering(all)

Cite this

Entropy-based criterion in categorical clustering. / Li, Tao; Ma, Sheng; Ogihara, Mitsunori.

Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004. ed. / R. Greiner; D. Schuurmans. 2004. p. 536-543 (Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Li, T, Ma, S & Ogihara, M 2004, Entropy-based criterion in categorical clustering. in R Greiner & D Schuurmans (eds), Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004. Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004, pp. 536-543, Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004, Banff, Alta, Canada, 7/4/04.

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AB - Entropy-type measures for the heterogeneity of clusters have been used for a long time. This paper studies the entropy-based criterion in clustering categorical data. It first shows that the entropy-based criterion can be derived in the formal framework of probabilistic clustering models and establishes the connection between the criterion and the approach based on dissimilarity coefficients. An iterative Monte-Carlo procedure is then presented to search for the partitions minimizing the criterion. Experiments are conducted to show the effectiveness of the proposed procedure.

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ER -

Entropy-based criterion in categorical clustering

Abstract

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ASJC Scopus subject areas

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