A Hybrid Clustering Process using a Genetic Fuzzy System for the Knowledge Base of a Fuzzy Rule-Based System

Abstract

The present paper proposes a new Hybrid clustering Process based on Fuzzy Genetic System. The proposed Approach consists of two steps: (1) Using a method called Fuzzy clustering, all data elements will be clustered into N groups; (2) utilizing a Fuzzy Genetic System, for every level the fuzzy rule of adhesion will be generated. If we compare our research to others that use the hard clustering, we will conclude that by using the fuzzy clustering we are able to raise the ingredient of each cluster and upgrade the accuracy of the offer target system and we will win in terms of complexity because the system is based on hybrid intelligent method and then we will not need to generate a new cluster every time we add a new data point. Experimental results on estimation models using clustering methods on synthetic data show that the proposed algorithm outperforms few commonly used clustering algorithms.