| Citation: | Yunlong GAO, Chengyu YANG, Zhihao WANG, Sizhe LUO, Jinyan PAN. Robust Fuzzy C-means Clustering Algorithm Integrating Between-cluster Information[J]. Journal of Electronics & Information Technology, 2019, 41(5): 1114-1121. doi: 10.11999/JEIT180604
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Comparing with K-means, Fuzzy logic is introduced in Fuzzy C-Means to handle the information between clusters. It can obtain better cluster results. However, fuzzy logic makes observations could belong to more than just one cluster, which results FCM is especially sensitivity to the noisy and outlier and has poor generalization performance. So a Rrobust Fuzzy C-Means clustering integrated Between-cluster Information algorithm (RBI-FCM) is proposed. Taking advantage of the sparsity of K-means, RBI-FCM helps to reduce the interactions among different clusters and improve the separability of sample points which locate in the adjacent domains of different clusters. Beside minimizing the inner-cluster scattering condition, RBI-FCM considers the between-cluster information. The generalization performance of RBI-FCM can be improved. An effective iterative algorithm for solving the model is designed in this paper. The experimental results show that the RBI-FCM improves the robustness of FCM and reduce effectively its sensitivity to size-imbalance and differences on the distribution of clusters of FCM. The great clustering result is obtained.
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