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Since its introduction by Lingras and West a decade ago, rough k-means has gained increasing attention in academia as well as in practice. A recently introduced extension, π rough k-means, eliminates need for the weight parameter in rough k-means applying probabilities derived from Laplace’s Principle of Indifference. However, the proposal in its more general form makes it possible to optionally integrate user-defined weights for parameter tuning using techniques such as evolutionary computing. In this paper, we study the properties of this general user-weighted π k-means through extensive experiments.


School of Arts

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Conference Paper

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