Algorithms for sequential extraction of clusters by possibilistic clustering

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. © Springer-Verlag Berlin Heidelberg 2007.