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 - 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 |
Other
| Other | Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004 |
|---|---|
| Country | Canada |
| City | Banff, Alta |
| Period | 7/4/04 → 7/8/04 |
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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.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Entropy-based criterion in categorical clustering
AU - Li, Tao
AU - Ma, Sheng
AU - Ogihara, Mitsunori
PY - 2004
Y1 - 2004
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=14344259208&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=14344259208&partnerID=8YFLogxK
M3 - Conference contribution
SN - 1581138385
SP - 536
EP - 543
BT - Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004
A2 - Greiner, R.
A2 - Schuurmans, D.
ER -