Analysis of User-Weighted π Rough k-Means

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8818)

Abstract

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.

Keywords

Rough k-Means User-Defined Weights Soft Clustering 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Munich University of Applied Sciences, MunichGermany & Australian Catholic UniversitySydneyAustralia
  2. 2.Saint Mary’s UniversityHalifaxCanada

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