Improvement of data object's membership by using Fuzzy K-Means clustering approach

Abstract

Clustering is classified into two categories namely-Hard clustering and Soft clustering. The hard clustering restricts that the data object in the given data belongs to exactly one cluster. The problem with hard K-Means (KM) clustering is that the different initial partitions can result in different final clusters. Soft clustering which also known as fuzzy clustering forms clusters such that data object can belong to more than one cluster based on their membership values. But sometimes the resulting membership values do not always correspond well to the degrees of belonging of the data. So to overcome the problems in hard K-means (KM) clustering, the Fuzzy K-Means (FKM) clustering approach is proposed. The Proposed Fuzzy K-Means clustering assigns membership to an object inversely related to the relative distance of the object to cluster prototype. Fuzzy clustering uses membership values to assign data objects to one or more clusters. The membership values indicate the strength of the association between that data object and a particular cluster. The proposed work also compares the execution time and required memory of Proposed Fuzzy K-Means (FKM) to that of hard K-means (KM) clustering. The result shows membership of data object is improved, also the execution time and memory required for Proposed Fuzzy K-Means (FKM) clustering is less than that of hard K-Means (KM) clustering.