New Information-based Clustering Method using Renyi's Entropy and Fuzzy C-Means Clustering

M. Aghagolzadeh (Iran), H. Soltanian-Zadeh (Iran/USA), and B.N. Araabi (Iran)


Information theory, Renyi’s entropy, Top-down hierarchical algorithms, clustering.


: This paper presents a new clustering method based on Renyi entropy. The proposed method maximizes entropy of clusters using between and within clusters entropies. It is a top-down multi-resolution method and uses the initial clusters found by Fuzzy C Means. Applications of the proposed algorithm on the synthetic data are compared with those of C-Means and Gustafson-Kessel algorithms. Results show superiority of the proposed algorithm to these methods.

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