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Weighting variables in K-means clustering

Author

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  • Myung-Hoe Huh
  • Yong Lim

Abstract

The aim of this study is to assign weights w1, …, wm to m clustering variables Z1, …, Zm, so that k groups were uncovered to reveal more meaningful within-group coherence. We propose a new criterion to be minimized, which is the sum of the weighted within-cluster sums of squares and the penalty for the heterogeneity in variable weights w1, …, wm. We will present the computing algorithm for such k-means clustering, a working procedure to determine a suitable value of penalty constant and numerical examples, among which one is simulated and the other two are real.

Suggested Citation

  • Myung-Hoe Huh & Yong Lim, 2009. "Weighting variables in K-means clustering," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(1), pages 67-78.
    • Handle: RePEc:taf:japsta:v:36:y:2009:i:1:p:67-78
    DOI: 10.1080/02664760802382533
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    References listed on IDEAS

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    1. Glenn Milligan & Martha Cooper, 1988. "A study of standardization of variables in cluster analysis," Journal of Classification, Springer;The Classification Society, vol. 5(2), pages 181-204, September.
    2. Wayne DeSarbo & J. Carroll & Linda Clark & Paul Green, 1984. "Synthesized clustering: A method for amalgamating alternative clustering bases with differential weighting of variables," Psychometrika, Springer;The Psychometric Society, vol. 49(1), pages 57-78, March.
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