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Fuzzy C-means algorithm (Fuzzy C-means, FCM) is one of the most popular fuzzy clustering method because of its simplicity and effectiveness, but FCM is sensitive to the initial cluster centers and noises. Possibility C-means algorithm (Possibilistic C-means, PCM) can pay for its freedom to ignore noise points. However, PCM is also very sensitive to initializations, and it sometimes generates coincident clusters. In order to overcome the weakness, a hybrid C-means fuzzy clustering algorithm, which combines FCM and PCM, is presented by the introduction of Mercer Kernel method and Particle Swarm Optimization (PSO).
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