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 | Canada |
| City | Banff, Alta |
| Period | 7/4/04 → 7/8/04 |
ASJC Scopus subject areas
- Engineering(all)
Fingerprint Dive into the research topics of 'Entropy-based criterion in categorical clustering'. Together they form a unique fingerprint.
Cite this
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 (pp. 536-543). (Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004).