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Please use this identifier to cite or link to this item:

http://hdl.handle.net/20.500.12358/24896
TitleInitializing k-means clustering algorithm using statistical information
Untitled
Abstract

K-means clustering algorithm is one of the best known algorithms used in clustering; nevertheless it has many disadvantages as it may converge to a local optimum, depending on its random initialization of prototypes. We will propose an enhancement to the initialization process of k-means, which depends on using statistical information from the data set to initialize the prototypes. We show that our algorithm gives valid clusters, and that it decreases error and time.

Authors
Eltibi, Mohammad F
Ashour, Wesam M.
TypeJournal Article
Date2011
Subjects
unsupervised learning
data clustering clustering
k-means clustering
initial prototypes determination
normal distribution
central limit theory
maximum likelihood estimator
general terms data mining
Published inInternational Journal of Computer Applications
SeriesVolume: 29, Number: 7
PublisherInternational Journal of Computer Applications, 244 5 th Avenue,# 1526, New York, NY 10001, USA India
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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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