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.