Research Article Open Access

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

Hamedoun Lamiae1, Attariuas Hicham1 and Ben Maati Mohamed Larbi1
  • 1 Abdelmalek Essaadi University, Morocco

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.

Journal of Computer Science
Volume 12 No. 12, 2016, 572-581

DOI: https://doi.org/10.3844/jcssp.2016.572.581

Submitted On: 4 April 2016 Published On: 6 February 2017

How to Cite: Lamiae, H., Hicham, A. & Larbi, B. M. M. (2016). A Hybrid Clustering Process using a Genetic Fuzzy System for the Knowledge Base of a Fuzzy Rule-Based System. Journal of Computer Science, 12(12), 572-581. https://doi.org/10.3844/jcssp.2016.572.581

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Keywords

  • Fuzzy Clustering
  • Genetic Fuzzy System
  • Back Propagation Network
  • Hybrid Intelligence Approach