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A hybrid clustering algorithm based on PSO with dynamic crossover

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Abstract

In order to overcome the premature convergence in particle swarm optimization (PSO), we introduce dynamical crossover, a crossover operator with variable lengths and positions, to PSO, which is briefly denoted as CPSO. To get rid of the drawbacks of only finding the convex clusters and being sensitive to the initial points in \(k\)-means algorithm, a hybrid clustering algorithm based on CPSO is proposed. The difference between the work and the existing ones lies in that CPSO is firstly introduced into \(k\)-means. Experimental results performing on several data sets illustrate that the proposed clustering algorithm can get completely rid of the shortcomings of \(k\)-means algorithms, and acquire correct clustering results. The application in image segmentation illustrates that the proposed algorithm gains good performance.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No.61272119, No.61203372), and the Fundamental Research Funds for the Central Universities (No. K50510030014).

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Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, 710071, People’s Republic of China

    Jie Zhang & Yuping Wang

  2. Department of Computer Science and Technology, Guangzhou University Sontan College, Zengcheng, 511370, Guangzhou, People’s Republic of China

    Jie Zhang & Junhong Feng

Authors
  1. Jie Zhang
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  2. Yuping Wang
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  3. Junhong Feng
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Corresponding authors

Correspondence to Jie Zhang or Yuping Wang.

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Communicated by D. Liu.

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Zhang, J., Wang, Y. & Feng, J. A hybrid clustering algorithm based on PSO with dynamic crossover. Soft Comput 18, 961–979 (2014). https://doi.org/10.1007/s00500-013-1115-6

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