A Novel Multimodal Probability Model for Cluster Analysis

Authors:
Jian Yu;Miin-Shen Yang;Pengwei Hao
Affiliations:
Dept. of Computer Science, Beijing Jiaotong University, Beijing, China;Dept. of Applied Maths, Chung Yuan Christian University, Chung-Li, Taiwan 32023;Center of Information Science, Peking University, Beijing, China 100871
Venue:
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Year:
2009

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Abstract

Cluster analysis is a tool for data analysis. It is a method for finding clusters of a data set with most similarity in the same group and most dissimilarity between different groups. In general, there are two ways, mixture distributions and classification maximum likelihood method, to use probability models for cluster analysis. However, the corresponding probability distributions to most clustering algorithms such as fuzzy c-means, possibilistic c-means, mode-seeking methods, etc., have not yet been found. In this paper, we construct a multimodal probability distribution model and then present the relationships between many clustering algorithms and the proposed model via the maximum likelihood estimation.