Possibility theoretic clustering

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. © Springer-Verlag Berlin Heidelberg 2005.