A New Algorithm for Fuzzy Clustering Able to Find the Optimal Number of Clusters

Abstract

Tackling, within a classification task, to the problem of inaccuracy explains the development of new theories that offer a formal treatment of imprecise information, especially the theory of fuzzy sets who suggested a new approach taking advantage of the concept of membership function. Nevertheless, clustering algorithms still show limits, particularly for the estimation of the number of clusters. In this paper, through a state of the art of the main fuzzy classification algorithms, we introduce a new algorithm, called Fuzzy-MSOM. The latter aims at palliating to drawback of the determination of the suitable number of clusters in a given data set. Thus, the clustering process is carried out through a multi-level approach. Through the use of fuzzy clustering validity indices, Fuzzy-MSOM overcomes the problem of the estimation of clusters number. The experimental result shows that the proposed clustering technique provides better results compared to the previous algorithms.