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Performance Comparison of Fuzzy C Means with Respect to Other Clustering Algorithm

Fuzzy C-Mean (FCM) is an unsupervised clustering algorithm based on fuzzy set theory that allows an element to belong to more than one cluster. Where fuzzy means “unclear” or “not defined” and c denotes “clustering”. In FCM the number of cluster are randomly selected. [15] FCM is the advanced version of K-means clustering algorithm and doing more work than K-means. K-Means just needs to do a distance calculation, whereas fuzzy c means needs to do a full inverse-distance weighting. This, plus the overhead needed for computing and managing, explains why FCM is quite slower than K-Means.[4]