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http://hdl.handle.net/20.500.12358/24555
TitleImproving Bregman k-means
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Abstract

We review Bregman divergences and use them in clustering algorithms which we have previously developed to overcome one of the difficulties of the standard k-means algorithm which is its sensitivity to initial conditions which leads to finding sub-optimal local minima. We show empirical results on artificial and real datasets.

Authors
Ashour, Wesam M.
Fyfe, Colin
TypeJournal Article
Date2014
Subjects
k-means
bregman divergences
clustering
local optima
Published inInternational Journal of Data Mining, Modelling and Management
SeriesVolume: 6, Number: 1
PublisherInderscience Publishers Ltd
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  • Staff Publications- Faculty of Engineering [865]
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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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