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

نویسندگان
Fazel Zarandi, M. H. ID 1
Karbasian, M. ID 2
Faraji, M. R. ID 3

1 Department of Industrial Engineering,Amirkabir University of Technology

2 Department of Industrial Engineering,Amirkabir University of Technology

3 Department of Industrial Engineering,Amirkabir University of Technology

doi
چکیده

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 e ective and robust under different conditions of data sets, such as noisy environments and large data sets.

کلیدواژه‌ها
Fuzzy clustering, Cluster validity index, Fuzzy c-mean algorithm, Fuzzy k-numbers clustering, Fuzzy numbers, Compactness, separation