%0 Journal Article
%T An Exponential Cluster Validity Index for Fuzzy Clustering with Crisp and Fuzzy Data
%J Scientia Iranica
%I Sharif University of Technology
%Z 1026-3098
%A Fazel Zarandi, M. H.
%A Faraji, M. R.
%A Karbasian, M.
%D 2010
%\ 12/01/2010
%V 17
%N 2
%P -
%! An Exponential Cluster Validity Index for Fuzzy Clustering with Crisp and Fuzzy Data
%K Fuzzy clustering
%K Cluster validity index
%K Fuzzy c-mean algorithm
%K Fuzzy k-numbers clustering
%K Fuzzy numbers
%K Compactness
%K separation
%R
%X 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 eective and robust under
different conditions of data sets, such as noisy environments and large data sets.
%U https://scientiairanica.sharif.edu/article_3359_05096886dbe7a956b1c4ad9b2de6ab0a.pdf