Data Classification by using Homogeneous Clustering Process

Abstract

In this paper we have performed dynamic clustering based on classification. The Enhance Neuro-fuzzy system for classification using dynamic clustering presented in this paper is an extension of the original Neuro-fuzzy method for linguistic feature selection and rule-based classification. In the dynamic clustering process, the parameter values like centroid, threshold value and standard deviation are estimated. This parameter is used for creating the cluster. The Gaussian membership function is applied to these clusters to generate the binary value of each feature to given cluster. Using this method we have got the large number of cluster and minimum accuracy. To reduce the cluster size and to improve the accuracy we have implement the homogenous clustering process. Using this process we have minimize the cluster size, and also improve the accuracy. General Terms Fuzzy logic, Artificial intelligence, Neural Network.