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.
Cite
Text
Li et al. "Entropy-Based Criterion in Categorical Clustering." International Conference on Machine Learning, 2004. doi:10.1145/1015330.1015404Markdown
[Li et al. "Entropy-Based Criterion in Categorical Clustering." International Conference on Machine Learning, 2004.](https://mlanthology.org/icml/2004/li2004icml-entropy/) doi:10.1145/1015330.1015404BibTeX
@inproceedings{li2004icml-entropy,
title = {{Entropy-Based Criterion in Categorical Clustering}},
author = {Li, Tao and Ma, Sheng and Ogihara, Mitsunori},
booktitle = {International Conference on Machine Learning},
year = {2004},
doi = {10.1145/1015330.1015404},
url = {https://mlanthology.org/icml/2004/li2004icml-entropy/}
}