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Weighting variables in K-means clustering
- Weighting variables in K-means clustering
- Huh M.-H.; Lim Y.
- Ewha Authors
- SCOPUS Author ID
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- Journal Title
- Journal of Applied Statistics
- Journal of Applied Statistics vol. 36, no. 1, pp. 67 - 78
- SCIE; SCOPUS
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- The aim of this study is to assign weights w 1,..., wm to m clustering variables Z 1,..., Z m, 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 w 1,..., w m. 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.
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