A Genetic K-means Clustering Algorithm Based on the Optimized Initial Centers

  •  Min Feng    
  •  Zhenyan Wang    


An optimized initial center of K-means algorithm(PKM) is proposed, which select the k furthest distance data in the high-density area as the initial cluster centers. Experiments show that the algorithm not only has a weak dependence on the initial data, but also has fast convergence and high clustering quality. To obtain effective cluster and accurate cluster, we combine the optimized K-means algorithm(PKM) and genetic algorithm into a hybrid algorithm (PGKM). It can not only improve compactness and separation of the algorithm but also automatically search for the best cluster number k, then cluster after optimizing the k-centers. The optimal cluster is not obtained until terminal conditions are met after continuously iterating. Experiments show that the algorithm has good cluster quality and overall performance.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1913-8989
  • ISSN(Online): 1913-8997
  • Started: 2008
  • Frequency: quarterly

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