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PROJECT TITLE :

Improving fuzzy c-means method for unbalanced dataset

ABSTRACT:

Traditional fuzzy c-means that method (FCM) may be a famous clustering algorithm, but incorporates a poor clustering performance for unbalanced dataset. To tackle this defect, a brand new FCM is presented by introducing cluster size into the formula of determining the membership values in every iteration. Experimental results on synthetic and UCI datasets showed that the proposed method has a better clustering performance than traditional FCM in terms of managing datasets with unbalanced clusters.


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