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Clustering through empirical likelihood ratio

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  • Melnykov, Volodymyr
  • Shen, Gang

Abstract

There is a vast variety of clustering methods available in the literature. The performance of many of them strongly depends on specific patterns in data. This paper introduces a clustering procedure based on the empirical likelihood method which inherits many advantages of the classical likelihood approach without imposing restrictive probability distribution constraints. The performance of the proposed procedure is illustrated on simulated and classification datasets with excellent results. The comparison of the algorithm with several well-known clustering methods is very encouraging. The procedure is more robust and has higher accuracy than the competitors.

Suggested Citation

  • Melnykov, Volodymyr & Shen, Gang, 2013. "Clustering through empirical likelihood ratio," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 1-10.
    • Handle: RePEc:eee:csdana:v:62:y:2013:i:c:p:1-10
    DOI: 10.1016/j.csda.2012.12.011
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    References listed on IDEAS

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    Cited by:

    1. Volodymyr Melnykov & Xuwen Zhu, 2019. "An extension of the K-means algorithm to clustering skewed data," Computational Statistics, Springer, vol. 34(1), pages 373-394, March.

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