A two-leveled symbiotic evolutionary algorithm for clustering problems

  • Authors:

  • Kyoung Seok Shin;Young-Seon Jeong;Myong K. Jeong

  • Affiliations:

  • Rutgers Center for Operations Research (RUTCOR), Rutgers, The State University of New Jersey, Piscataway, USA;Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, Piscataway, USA;Rutgers Center for Operations Research (RUTCOR), Rutgers, The State University of New Jersey, Piscataway, USA and Department of Industrial and Systems Engineering, Rutgers, The State University of ...

  • Venue:
  • Applied Intelligence
  • Year:
  • 2012

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Abstract

Because of its unsupervised nature, clustering is one of the most challenging problems, considered as a NP-hard grouping problem. Recently, several evolutionary algorithms (EAs) for clustering problems have been presented because of their efficiency for solving the NP-hard problems with high degree of complexity. Most previous EA-based algorithms, however, have dealt with the clustering problems given the number of clusters (K) in advance. Although some researchers have suggested the EA-based algorithms for unknown K clustering, they still have some drawbacks to search efficiently due to their huge search space. This paper proposes the two-leveled symbiotic evolutionary clustering algorithm (TSECA), which is a variant of coevolutionary algorithm for unknown K clustering problems. The clustering problems considered in this paper can be divided into two sub-problems: finding the number of clusters and grouping the data into these clusters. The two-leveled framework of TSECA and genetic elements suitable for each sub-problem are proposed. In addition, a neighborhood-based evolutionary strategy is employed to maintain the population diversity. The performance of the proposed algorithm is compared with some popular evolutionary algorithms using the real-life and simulated synthetic data sets. Experimental results show that TSECA produces more compact clusters as well as the accurate number of clusters.