On initializations for the minkowski weighted k-means

Authors:
Renato Cordeiro de Amorim;Peter Komisarczuk
Affiliations:
Department of Computer Science and Information Systems, Birkbeck University of London, UK,School of Computing and Technology, University of West London, UK;School of Computing and Technology, University of West London, UK
Venue:
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Year:
2012

Citing 7
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MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I

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Abstract

Minkowski Weighted K-Means is a variant of K-Means set in the Minkowski space, automatically computing weights for features at each cluster. As a variant of K-Means, its accuracy heavily depends on the initial centroids fed to it. In this paper we discuss our experiments comparing six initializations, random and five other initializations in the Minkowski space, in terms of their accuracy, processing time, and the recovery of the Minkowski exponent p. We have found that the Ward method in the Minkowski space tends to outperform other initializations, with the exception of low-dimensional Gaussian Models with noise features. In these, a modified version of intelligent K-Means excels.