@inproceedings{f973591f24904fcbb660b4fa98f13672,
title = "Entropy-based criterion in categorical clustering",
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.",
author = "Tao Li and Sheng Ma and Mitsunori Ogihara",
year = "2004",
month = dec,
day = "1",
language = "English (US)",
isbn = "1581138385",
series = "Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004",
pages = "536--543",
editor = "R. Greiner and D. Schuurmans",
booktitle = "Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004",
note = "Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004 ; Conference date: 04-07-2004 Through 08-07-2004",
}