An Exponential Cluster Validity Index for Fuzzy Clustering with Crisp and Fuzzy Data

作者：Zarandi M H Fazel^*; Faraji M R; Karbasian M

来源：Scientia Iranica, 2010, 17(2): 95-110.

• Fuzzy clustering
• Cluster validity index
• Fuzzy c-mean algorithm
• Fuzzy k-numbers clustering
• Fuzzy numbers
• Compactness
• Separation

This paper presents a new cluster validity index for finding a suitable number of fuzzy clusters with crisp and fuzzy data. The new index, called the ECAS-index, contains exponential compactness and separation measures. These measures indicate homogeneity within clusters and heterogeneity between clusters, respectively. Moreover, a fuzzy c-mean algorithm is used for fuzzy clustering with crisp data, and a fuzzy k-numbers clustering is used for clustering with fuzzy data. In comparison to other indices, it is evident that the proposed index is more effective and robust under different conditions of data sets, such as noisy environments and large data sets.

• 出版日期2010-12