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An Enhanced Fuzzy Possibilistic C-means with Repulsion and Cluster Validity Index


D.Vanisri, C.Loganathan


Vol. 11  No. 2  pp. 197-207


The rapid worldwide increase in the data available leads to the difficulty for analyzing those data. Organizing data into interesting collection is one of the most basic forms of understanding and learning. Thus, a proper data mining approach is required to organize those data for better understanding. Clustering is one of the standard approaches in the field of data mining. The main of this approach is to organize a dataset into a set of clusters, which consists of “similar” data items, as calculated by some distance function. There are various clustering techniques like K-Means, Possibilistic C-Mean, etc., proposed by various researchers. Recently, Fuzzy Possibilistic C-Means is found to be better because of its embedded fuzzy logic. This paper initially proposed a Modified Fuzzy Possibilistic C-Means (MFPCM) algorithm which enhances the clustering accuracy. Next, Penalized and Compensated constraints are used in the objective function. For further improvement in clustering accuracy, Repulsion term is introduced in the objective function. Finally, Cluster Validity Index is performed by using Partition Coefficient and Exponential Separation (PCAES) method. The experimental result shows that the proposed clustering technique results in lesser error rate which in turn shows the better accuracy of classification.


Fuzzy Possibilistic C-Means, Modified Fuzzy Possibilistic C-Means, Penalized and Compensated Constraints, Repulsion, Partition Coefficient and Exponential Separation