文件名称:9552010E202
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This paper presents a new cluster validity index for nding 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 ffective and robust under
di fferent conditions of data sets, such as noisy environments and large data sets.
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 ffective and robust under
di fferent conditions of data sets, such as noisy environments and large data sets.
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9552010E202.pdf