A New Cluster Validity Index for Fuzzy Clustering
本文提出了一个在模糊聚类中判别聚类有效性的新指标。该指标可有效地对类间有交叠或有多孤立点的情况做出准确的判定。文中基于模糊C-均值聚类算法(FCM),应用多组的测试数据对其进行了性能分析,并与当前较广泛使用且较具代表性的某些相关指标进行了深入的比较。实验结果表明,该指标函数的判定性能是优越的,它可以自动地确定聚类的最佳个数。In this paper, we propose a new validity index for determining the number of clusters. It is based on a novel way of combining cohesion and discrepancy. Extensive tests of the index in a conventional model selection process (FCM algorithm) have been performed using generated data sets and public domain data sets,and comparison with several existing and important indices has been made. The results obtained show clearly the efficiency of the new index under the condition of overlapping clusters.