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A New Soft Computing Method for K-Harmonic Means Clustering

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  • Wei-Chang Yeh
  • Yunzhi Jiang
  • Yee-Fen Chen
  • Zhe Chen

Abstract

The K-harmonic means clustering algorithm (KHM) is a new clustering method used to group data such that the sum of the harmonic averages of the distances between each entity and all cluster centroids is minimized. Because it is less sensitive to initialization than K-means (KM), many researchers have recently been attracted to studying KHM. In this study, the proposed iSSO-KHM is based on an improved simplified swarm optimization (iSSO) and integrates a variable neighborhood search (VNS) for KHM clustering. As evidence of the utility of the proposed iSSO-KHM, we present extensive computational results on eight benchmark problems. From the computational results, the comparison appears to support the superiority of the proposed iSSO-KHM over previously developed algorithms for all experiments in the literature.

Suggested Citation

  • Wei-Chang Yeh & Yunzhi Jiang & Yee-Fen Chen & Zhe Chen, 2016. "A New Soft Computing Method for K-Harmonic Means Clustering," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-14, November.
    • Handle: RePEc:plo:pone00:0164754
    DOI: 10.1371/journal.pone.0164754
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    References listed on IDEAS

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    1. Huang, Chia-Ling, 2015. "A particle-based simplified swarm optimization algorithm for reliability redundancy allocation problems," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 221-230.
    2. Lee, Ji-Hyun & Yeh, Wei-Chang & Chuang, Mei-Chi, 2015. "Web page classification based on a simplified swarm optimization," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 13-24.
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