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- W4383872018 abstract "Anticlustering refers to the process of partitioning elements into disjoint groups with the goal of obtaining high between-group similarity and high within-group heterogeneity. Anticlustering thereby reverses the logic of its better known twin-cluster analysis-and is usually approached by maximizing instead of minimizing a clustering objective function. This paper presents k-plus, an extension of the classical k-means objective of maximizing between-group similarity in anticlustering applications. K-plus represents between-group similarity as discrepancy in distribution moments (means, variance, and higher-order moments), whereas the k-means criterion only reflects group differences with regard to means. While constituting a new criterion for anticlustering, it is shown that k-plus anticlustering can be implemented by optimizing the original k-means criterion after the input data have been augmented with additional variables. A computer simulation and practical examples show that k-plus anticlustering achieves high between-group similarity with regard to multiple objectives. In particular, optimizing between-group similarity with regard to variances usually does not compromise similarity with regard to means; the k-plus extension is therefore generally preferred over classical k-means anticlustering. Examples are given on how k-plus anticlustering can be applied to real norming data using the open source R package anticlust, which is freely available via CRAN." @default.
- W4383872018 created "2023-07-12" @default.
- W4383872018 creator A5043644581 @default.
- W4383872018 date "2023-07-11" @default.
- W4383872018 modified "2023-10-14" @default.
- W4383872018 title "K‐Plus anticlustering: An improved <i>k</i>‐means criterion for maximizing between‐group similarity" @default.
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- W4383872018 doi "https://doi.org/10.1111/bmsp.12315" @default.
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