Genetic algorithm-tuned entropy-based fuzzy C-means algorithm for obtaining distinct and compact clusters

  • Authors:

  • Vidyut Dey;Dilip Kumar Pratihar;G. L. Datta

  • Affiliations:

  • Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, India 721 302;Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, India 721 302;Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, India 721 302

  • Venue:
  • Fuzzy Optimization and Decision Making
  • Year:
  • 2011

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Abstract

A modified approach had been developed in this study by combining two well-known algorithms of clustering, namely fuzzy c-means algorithm and entropy-based algorithm. Fuzzy c-means algorithm is one of the most popular algorithms for fuzzy clustering. It could yield compact clusters but might not be able to generate distinct clusters. On the other hand, entropy-based algorithm could obtain distinct clusters, which might not be compact. However, the clusters need to be both distinct as well as compact. The present paper proposes a modified approach of clustering by combining the above two algorithms. A genetic algorithm was utilized for tuning of all three clustering algorithms separately. The proposed approach was found to yield both distinct as well as compact clusters on two data sets.