A Modified Fuzzy C Means Algorithm for Clustering Based On Density Sensitive Measure

Abstract

The major objective of clustering is to discover collection of comparable objects based on similarity metric. A similarity metric is generally specified by the user according to the requirements for obtaining better results. The distance between the measures of two objects in a particular cluster should be well defined using effective distance measures. There are several approaches available for clustering objects. But these techniques are not suitable for all applications and huge data collections. In the proposed approach an effective fuzzy clustering technique is used. Fuzzy C-Means (FCM) is the effective clustering algorithm available to cluster unlabeled data that produces both membership and typicality values during clustering process. Density sensitive measures are embedded with the FCM method’s objective function to construct the Modified FCM. The proposed modified Fuzzy C-Means clustering algorithm will help in better calculation of distance between the clusters and increasing the accuracy of clustering. The performance of the proposed approaches is evaluated on the University of California, Irvine (UCI) machine repository datasets such as Iris, Wine, Lung Cancer and Lymphography. The parameters used for the evaluation is Clustering accuracy, Mean Squared Error (MSE), Execution Time.