An improved fuzzy k-medoids clustering algorithm with optimized number of clusters

K-medoids algorithm is one of the most prominent techniques, as a partitioning clustering algorithm, in data mining and knowledge discovery applications. However, the determined numbers of cluster as an input and the impact of initial value of cluster centers on clusters´ quality are the two major challenges of this algorithm. In this paper an improved version of fuzzy k-medoids algorithm has been proposed. Applying entropy concept as a complementary factor in optimization problem of fuzzy k-medoids has become to obtain more accurate centers. Also, using this factor, number of clusters has been achieved effectively. The results show that the proposed method outperforms fuzzy k-medoids in terms of accuracy of obtained centers.