On initializations for the Minkowski Weighted K-Means

Amorim, Renato Cordeiro de and Komisarczuk, Peter (2012) On initializations for the Minkowski Weighted K-Means. In: Advances in Intelligent Data Analysis XI. IDA 2012. Lecture Notes in Computer Science, 7619 . Springer, Heidelberg, Germany, pp. 45-55. ISBN 9783642341557

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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.

Item Type: Book Section
Uncontrolled Keywords: Minkowski K-Means, K-Means Initializations, Lp Space, Minkowski Space, Feature Weighting, Noise Features, intelligent K-Means, Ward Method
Subjects: Computing
Depositing User: Vani Aul
Date Deposited: 21 Mar 2014 16:07
Last Modified: 29 Mar 2017 08:28
URI: http://repository.uwl.ac.uk/id/eprint/762

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