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- W1965600812 abstract "The aim of this study is to assign weights w 1, …, w m 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." @default.
- W1965600812 created "2016-06-24" @default.
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- W1965600812 date "2008-10-31" @default.
- W1965600812 modified "2023-10-05" @default.
- W1965600812 title "Weighting variables in K-means clustering" @default.
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- W1965600812 doi "https://doi.org/10.1080/02664760802382533" @default.
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