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Simultaneous variable weighting and determining the number of clusters—A weighted Gaussian means algorithm

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  • Chakraborty, Saptarshi
  • Das, Swagatam

Abstract

We propose a simple variable (feature) weight learning strategy for the Gaussian means algorithm which can automatically determine the number of clusters in a dataset as well. We investigate some important theoretical properties and convergence behavior of the proposed algorithm.

Suggested Citation

  • Chakraborty, Saptarshi & Das, Swagatam, 2018. "Simultaneous variable weighting and determining the number of clusters—A weighted Gaussian means algorithm," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 148-156.
    • Handle: RePEc:eee:stapro:v:137:y:2018:i:c:p:148-156
    DOI: 10.1016/j.spl.2018.01.015
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    References listed on IDEAS

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    1. Paul D. McNicholas, 2016. "Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 331-373, October.
    2. O’Hagan, Adrian & Murphy, Thomas Brendan & Gormley, Isobel Claire & McNicholas, Paul D. & Karlis, Dimitris, 2016. "Clustering with the multivariate normal inverse Gaussian distribution," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 18-30.
    3. Jeffrey Andrews & Paul McNicholas, 2014. "Variable Selection for Clustering and Classification," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 136-153, July.
    4. Renato Cordeiro Amorim, 2016. "A Survey on Feature Weighting Based K-Means Algorithms," Journal of Classification, Springer;The Classification Society, vol. 33(2), pages 210-242, July.
    5. Fischer, Aurélie, 2011. "On the number of groups in clustering," Statistics & Probability Letters, Elsevier, vol. 81(12), pages 1771-1781.
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