[Objective] This paper tries to solve the issues facing traditional FCM algorithm, such as randomly choosing initial cluster center, sensitive to noise, and only capable of clustering the equally distributed samples. [Methods] We proposed a new FCM clustering algorithm based on Huffman tree with dissimilarity degree matrix of high density sample sets. The new algorithm could get initial clustering centers, and then generate the membership function of the non-normalized constraint samples. [Results] We examined the proposed algorithm with man-made samples, images, and UCI datasets. The clustering accuracy and the computation time of the new algorithm were better than algorithms based on the Gauss kernel or traditional FCM. [Limitations] The $\beta $ of the sample density adjustment factor was decided by experiment or experience without theoretical supports. [Conclusions] The proposed algorithm could be used for clustering data sets with high level of noise and distributed unequally.
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