Because traditional fuzzy clustering validity indices need to specify the number of clusters and are sensitive to noise data, we\npropose a validity index for fuzzy clustering, named CSBM (compactness separateness bipartite modularity), based on bipartite\nmodularity. CSBM enhances the robustness by combining intraclass compactness and interclass separateness and can automatically\ndetermine the optimal number of clusters. In order to estimate the performance of CSBM, we carried out experiments on\nsix real datasets and compared CSBM with other six prominent indices. Experimental results show that the CSBM index performs\nthe best in terms of robustness while accurately detecting the number of clusters.
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