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Erschienen in:
2014 | OriginalPaper | Buchkapitel
A New Fuzzy Clustering Validity Index with Strong Robustness
verfasst von : Xiang-Jun Xie, Yong Wang, Li Zhong
Erschienen in: Fuzzy Information & Engineering and Operations Research & Management
Verlag: Springer Berlin Heidelberg
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Abstract
Cluster validity has been widely used to evaluate the fitness of partitions produced by fuzzy c-means (FCM) clustering algorithm. Many validity functions have been proposed for evaluating clustering results. Most of these popular validity measures do not work well for clusters with different fuzzy weighting exponent \(m\) and data with outliers at the same time. In this paper, we propose a new validity index for fuzzy clustering. This validity index is based on the compactness and separation measure. The compactness is defined by fuzzy Z-membership function based on the gold dividing point and separation is described by monotone linear function. The contrasting experimental results show that the proposed index works well.