Department of Industrial Engineering,Amirkabir University of Technology
Abstract
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.
Fazel Zarandi, M. H., Faraji, M. R., & Karbasian, M. (2010). An Exponential Cluster Validity Index for Fuzzy Clustering with Crisp and Fuzzy Data. Scientia Iranica, 17(2), -.
MLA
M. H. Fazel Zarandi; M. R. Faraji; M. Karbasian. "An Exponential Cluster Validity Index for Fuzzy Clustering with Crisp and Fuzzy Data". Scientia Iranica, 17, 2, 2010, -.
HARVARD
Fazel Zarandi, M. H., Faraji, M. R., Karbasian, M. (2010). 'An Exponential Cluster Validity Index for Fuzzy Clustering with Crisp and Fuzzy Data', Scientia Iranica, 17(2), pp. -.
VANCOUVER
Fazel Zarandi, M. H., Faraji, M. R., Karbasian, M. An Exponential Cluster Validity Index for Fuzzy Clustering with Crisp and Fuzzy Data. Scientia Iranica, 2010; 17(2): -.
)