Possibility theoretic clustering

Authors:
Shitong Wang;Fu-lai Chung;Min Xu;Dewen Hu;Lin Qing
Affiliations:
Dept. of Computing, Hong Kong Polytechnic University, Hong Kong, China;Dept. of Computing, Hong Kong Polytechnic University, Hong Kong, China;School of Information Engineering, Southern Yangtze University, China;School of Automation, National Defense University of Science and Technology, Changsha, China;School of Information Engineering, Southern Yangtze University, China
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

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Abstract

Based on the exponential possibility model, the possibility theoretic clustering algorithm is proposed in this paper. The new algorithm is distinctive in determining an appropriate number of clusters for a given dataset while obtaining a quality clustering result. The proposed algorithm can be easily implemented using an alternative minimization iterative procedure and its parameters can be effectively initialized by the Parzon window technique and Yager's probability-possibility transformation. Our experimental results demonstrate its success in artificial datasets.