An Improved Hybrid Genetic Clustering Algorithm

  • Yongguo Liu
  • Jun Peng
  • Kefei Chen
  • Yi Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3955)


In this paper, a new genetic clustering algorithm called IHGA-clustering is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGA-clustering, DHB operation is developed to improve the individual and accelerate the convergence speed, and partition-mergence mutation operation is designed to reassign objects among different clusters. Equipped with these two components, IHGA-clustering can stably output the proper result. Its superiority over HGA-clustering, GKA, and KGA-clustering is extensively demonstrated for experimental data sets.


Genetic Algorithm Time Complexity Convergence Speed Cluster Problem Mutation Operation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yongguo Liu
    • 1
    • 2
    • 3
  • Jun Peng
    • 4
  • Kefei Chen
    • 3
  • Yi Zhang
    • 1
  1. 1.College of Computer Science and EngineeringUniversity of Electronic, Science and Technology of ChinaChengduP.R. China
  2. 2.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingP.R. China
  3. 3.Department of Computer Science and EngineeringShanghai Jiaotong UniversityShanghaiP.R. China
  4. 4.School of Electronic Information EngineeringChongqing University of Science and TechnologyChongqingP.R. China

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