Algorithms for Sequential Extraction of Clusters by Possibilistic Clustering

Authors:
Sadaaki Miyamoto;Youhei Kuroda
Affiliations:
Department of Risk Engineering, Faculty of Systems and Information Engineering, University of Tsukuba, Ibaraki 305-8573, Japan;Graduate School of Systems and Information Engineering, University of Tsukuba, Ibaraki 305-8573, Japan
Venue:
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
Year:
2007

Citing 4
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Abstract

Possibilistic clustering that is robust to noise in data is another useful tool in addition to the best-known fuzzy c-means. However, there is a fundamental problem of strong dependence on initial values in possibilistic clustering and there is a proposal of an algorithm generating `one cluster at a time.' Moreover this method is related to the mountain clustering algorithm. In this paper these features are reconsidered and a number of algorithms of sequential generation of clusters which includes a possibilistic medoid clustering are proposed. These algorithms automatically determine the number of clusters. An illustrative example with different methods of sequential clustering is given.