A relaxation approach to the fuzzy clustering problem

In this paper a new algorithm for fuzzy clustering is presented. The proposed algorithm utilizes the idea of relaxation. Convergence of the proposed algorithm is proved and limits on the relaxation parameter are derived. Stopping criteria and resulting convergence behaviour of the algorithms are discussed. The performance of the new algorithm is compared to the fuzzy c-means algorithm by testing both on three published data sets. Theoretical and empirical results reported in this paper show that the new algorithm is more efficient and leads to significant computational savings.