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http://hdl.handle.net/20.500.12358/24455
TitleEfficient and fast initialization algorithm for k-means clustering
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Abstract

The famous K-means clustering algorithm is sensitive to the selection of the initial centroids and may converge to a local minimum of the criterion function value. A new algorithm for initialization of the K-means clustering algorithm is presented. The proposed initial starting centroids procedure allows the K-means algorithm to converge to a “better” local minimum. Our algorithm shows that refined initial starting centroids indeed lead to improved solutions. A framework for implementing and testing various clustering algorithms is presented and used for developing and evaluating the algorithm.

Authors
El Agha, Mohammed
Ashour, Wesam M.
TypeJournal Article
Date2012
Published inInternational Journal of Intelligent Systems and Applications
SeriesVolume: 4, Number: 1
PublisherModern Education and Computer Science Press
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  • Staff Publications- Faculty of Engineering [908]
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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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