Some of the well-known fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel (GK) clustering algorithm and Gath-Geva (GC...
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Some of the well-known fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel (GK) clustering algorithm and Gath-Geva (GC) clustering algorithm were developed to detect non-spherical structural clusters. Both, of GG and GK algorithms suffer from the singularity problem of covariance matrix and the effect of initial status. In this paper, a new Fuzzy C-Means algorithm, based, on Particle Swarm Optimization and Mahalanobis distance without prior information (pso-fcm-M) is proposed to improve those limitations of GG and GK algorithms. And we point out that the pso-fcm algorithm is a special case of pso-fcm-M algorithm. The experimental results of two real data sets show that the performance of our proposed pso-fcm-M algorithm is better than those of the fcm, GG, GK algorithms.
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