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Cluster validity index Is used for estimating the quality of partitions to a dataset by clustering algorithms, and finding the optimal number of clusters to be partitioned. In this paper, we propose a new validity index, which is based on a dispersion measure and an overlap measure. The dispersion measure estimates the overall data density of the clusters in the dataset; whereas the overlap measure...
One approach to image segmentation is to apply a data clustering method such as fuzzy c-means (FCM) to the pixels of the image. FCM and its variations all require an appropriately predefined number of clusters for a given set of data in order to obtain a correct clustering result However, an optimal number of clusters is usually unknown. Mok et al. proposed a robust adaptive clustering analysis method...
Fuzzy c-means (FCM) is one of the most popular techniques for data clustering. Since FCM tends to balance the number of data points in each cluster, centers of smaller clusters are forced to drift to larger adjacent clusters. For datasets with unbalanced clusters, the partition results of FCM are usually unsatisfactory. Cluster size insensitive FCM (csiFCM) dealt with “cluster-size sensitivity” problem...
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