A two-leveled symbiotic evolutionary algorithm for clustering problems

Kyoung Seok Shin, Young Seon Jeong, Myong K. Jeong

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)788-799
Number of pages12
JournalApplied Intelligence
Issue number4
StatePublished - Jun 1 2012

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


  • Clustering
  • Coevolutionary algorithm
  • Symbiotic evolutionary algorithm
  • Two-leveled structure

Fingerprint Dive into the research topics of 'A two-leveled symbiotic evolutionary algorithm for clustering problems'. Together they form a unique fingerprint.

Cite this