A new validation index for fuzzy clustering and its comparisons with other methods

This paper presents a new validation index for the Fuzzy C-Means algorithm, which is composed of two metrics, the modified partition entropy index and the sum of the distances between the means of the fuzzy partitions. The modified partition entropy represents the variation of the data in clusters of the dataset, and the sum of the distances between the means of the fuzzy partition represents the separation between clusters in the data set. The proposed index was tested with synthetic and benchmark datasets with good results.