The Global Fuzzy C-Means Clustering Algorithm

The fuzzy c-means (FCM) is one of the algorithms for clustering based on optimizing an objective function, being sensitive to initial conditions, the algorithm usually leads to local minimum results. Aiming at above problem, we present the global fuzzy c-means clustering algorithm (GFCM) which is an incremental approach to clustering. It does not depend on any initial conditions and the better clustering results are obtained through a deterministic global search procedure. We also propose the fast global fuzzy c-means clustering algorithm (FGFCM) to improve the converging speed of the global fuzzy c-means clustering algorithm. Experiments show that the global fuzzy c-means clustering algorithm can give us more satisfactory results by escaping from the sensibility to initial value and improving the accuracy of clustering; the fast global fuzzy c-means clustering algorithm improved the converging speed of the global fuzzy c-means clustering algorithm without significantly affecting solution quality.

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