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 effective and robust under

different conditions of data sets, such as noisy environments and large data sets.