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Selecting the Minkowski Exponent for Intelligent K-Means with Feature Weighting

In: Clusters, Orders, and Trees: Methods and Applications

Author

Listed:
  • Renato Cordeiro Amorim

    (Glyndŵr University)

  • Boris Mirkin

    (National Research University Higher School of Economics
    Birkbeck University of London)

Abstract

Recently, a three-stage version of K-Means has been introduced, at which not only clusters and their centers, but also feature weights are adjusted to minimize the summary p-th power of the Minkowski p-distance between entities and centroids of their clusters. The value of the Minkowski exponent p appears to be instrumental in the ability of the method to recover clusters hidden in data. This paper advances into the problem of finding the best p for a Minkowski metric-based version of K-Means, in each of the following two settings: semi-supervised and unsupervised. This paper presents experimental evidence that solutions found with the proposed approaches are sufficiently close to the optimum.

Suggested Citation

  • Renato Cordeiro Amorim & Boris Mirkin, 2014. "Selecting the Minkowski Exponent for Intelligent K-Means with Feature Weighting," Springer Optimization and Its Applications, in: Fuad Aleskerov & Boris Goldengorin & Panos M. Pardalos (ed.), Clusters, Orders, and Trees: Methods and Applications, edition 127, pages 103-117, Springer.
    • Handle: RePEc:spr:spochp:978-1-4939-0742-7_7
    DOI: 10.1007/978-1-4939-0742-7_7
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    Cited by:

    1. 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.

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