Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
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A symbiotic evolutionary algorithm for the clustering problems with an unknown number of clusters

클러스터 수가 주어지지 않는 클러스터링 문제를 위한 공생 진화알고리즘

  • Shin, Kyoung-Seok (Dept. of Industrial Engineering, Chonnam National University) ;
  • Kim, Jae-Yun (Dept. of Business Administration, Chonnam National University)
  • Received : 2011.01.07
  • Accepted : 2011.01.28
  • Published : 2011.03.31
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Abstract

Clustering is an useful method to classify objects into subsets that have some meaning in the context of a particular problem and has been applied in variety of fields, customer relationship management, data mining, pattern recognition, and biotechnology etc. This paper addresses the unknown K clustering problems and presents a new approach based on a coevolutionary algorithm to solve it. Coevolutionary algorithms are known as very efficient tools to solve the integrated optimization problems with high degree of complexity compared to classical ones. The problem considered in this paper can be divided into two sub-problems; finding the number of clusters and classifying the data into these clusters. To apply to coevolutionary algorithm, the framework of algorithm and genetic elements suitable for the sub-problems are proposed. Also, a neighborhood-based evolutionary strategy is employed to maintain the population diversity. To analyze the proposed algorithm, the experiments are performed with various test-bed problems which are grouped into several classes. The experimental results confirm the effectiveness of the proposed algorithm.

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References

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