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In this paper an optimization method for fuzzy c-means algorithm based on grid and density is proposed in order to solve the problem of initializing fuzzy c-means algorithm. Grid and density are needed to extract approximate clustering center from sample space. Then, an optimization method for fuzzy c-means algorithm is proposed by using amount of approximate clustering centers to initialize classification number, and using approximate clustering centers to initialize initial clustering centers. Experiment shows that this method can improve clustering result and shorten clustering time validly.

An Optimization Method for Fuzzy c-Means Algorithm Based on Grid and Density