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This paper presents a robust fuzzy clustering algorithm which can perform clustering without pre-assigning the number of clusters and is not sensitive to the initialization of cluster centers. This is achieved by iteratively splitting and merging operations under the guidance of mistake measurements. In every step of the iteration, we first split the cluster containing data points belonging to different...
A new validity index is proposed to determine the optimal number of clusters for fuzzy clustering. In a good partition, the similarity between data points within a cluster should be maximized and the clusters should be separated. Intra-cluster variation is defined to measure the similarity within a cluster; it should be minimized for a good cluster. Inter-cluster overlap is defined to measure the...
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