Novel Validity Index for Fuzzy Clustering
Huan Jiang · Jisuanji gongcheng · 2009
This paper proposes a novel validity index for fuzzy clustering.This index can determine the optimal partition and optimal number of clusters for fuzzy partitions obtained from the Fuzzy C-Means algorithm(FCM).It combines compactness and separation information of fuzzy clustering.The compactness is obtained by computing inter-cluster weighted the square of error.The separation is obtained by computing similarity between fuzzy clustering.Comparison experiment is done in three synthetical datasets and three real datasets,and the results prove that this index is superior effectiveness compared with other validity indexes.