Weighting variables in K-means clustering

Myung-Hoe Huh and Yong Lim

Journal of Applied Statistics, 2009, vol. 36, issue 1, 67-78

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.

Keywords: K-means clustering; variable weighting; penalty constant (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1080/02664760802382533

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