An Efficient and Fast Data Clustering using Fuzzy C-Means

Abstract

The chief objective of the clustering is to present a collection of similar records. Cluster Analysis (CA) is an exploratory data analysis technique for managing collected data into significant taxonomies, groups, or clusters, according to the combinations, which increases the similarity of cases inside a cluster at the same time increasing the dissimilarity between the other groups that are primarily unknown. In the proposed approach, the efficiency of the Modified Fuzzy C-means clustering is enhanced by density sensitive distance measure. Modified Fuzzy C-Means is the effective clustering algorithm available to cluster unlabeled data that produces both membership and typicality values during clustering process. The parameters used for the evaluation is Clustering accuracy, Mean Squared Error (MSE), Execution Time and Convergence behavior. The performance of the proposed approaches is evaluated, Irvine (UCI) machine repository datasets such as Iris, Wine, Lung Cancer and Lymphography.