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A cluster validity index is to evaluate the correct number of clusters when partitioning a dataset. In this paper, we propose a new cluster validity index based on two measures called dispersion and overlap for Gaussian-distributed clusters. The dispersion measure is used to estimate the situation of data spreading in a cluster. A small dispersion measure for a cluster means that data points are distributed...
In this paper, a validity index method VDOGK, a variation of the index method VDO, for estimating the optimal number of clusters in datasets with concave-/elongated-shaped clusters is presented. The new index uses Gustafson-Kessel FCM to partition the dataset so that geometric-shape-sensitivity problem of FCM can be reduced. It is based on both dispersion and overlap measures, where the dispersion...
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...
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