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dc.contributor.authorCordeiro De Amorim, Renato
dc.contributor.authorKomisarczuk, Peter
dc.identifier.citationCordeiro De Amorim , R & Komisarczuk , P 2012 , On Initializations for the Minkowski Weighted K-Means . in Advances in Intelligent Data Analysis XI . Lecture Notes in Computer Science , vol. 7619 , Springer Nature , pp. 45-55 , 11th Int Symposium, IDA 2012 , Helsinki , Finland , 25/10/12 .
dc.description.abstractMinkowski 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.en
dc.publisherSpringer Nature
dc.relation.ispartofAdvances in Intelligent Data Analysis XI
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.titleOn Initializations for the Minkowski Weighted K-Meansen
dc.contributor.institutionSchool of Computer Science

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