Effective Fuzzy Clustering Technique in Intelligent Decision Support System

This Paper is based on Fuzzy Logic Techniques and Clustering algorithms and their comparative study, to make huge dataset (clusters) according to the nature of data, using the different Fuzzy Clustering Techniques (FCM, Subtractive Clustering). We are representing the result here in a comparative manure on the basis of different factors like, cluster center and degree of Membership. Data Clustering is the process of dividing data elements into Classes or Clusters so that items in the same Class are as similar as possible and items in different Classes are as dissimilar as possible. In fuzzy Clustering, the data points can belong to more then one cluster and associated with each of the points are membership grades which indicate the degree to which the data points belong to the different clusters. The Fuzzy Clustering Techniques are used for the Research Work because this is most widely used for developing an intelligent system. The model conforms that the Subtractive Clustering algorithm is better then FCM (Fuzzy C-Means) algorithm by using the MATLAB (Matrix Laboratory) software environment.