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Reseach Article

Outlier Detection using Improved Genetic K-means

by M. H. Marghny, Ahmed I. Taloba
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 28 - Number 11
Year of Publication: 2011
Authors: M. H. Marghny, Ahmed I. Taloba

M. H. Marghny, Ahmed I. Taloba . Outlier Detection using Improved Genetic K-means. International Journal of Computer Applications. 28, 11 ( August 2011), 33-36. DOI=10.5120/3458-4723

@article{ 10.5120/3458-4723,
author = { M. H. Marghny, Ahmed I. Taloba },
title = { Outlier Detection using Improved Genetic K-means },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 28 },
number = { 11 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 33-36 },
numpages = {9},
url = { },
doi = { 10.5120/3458-4723 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:14:33.244841+05:30
%A M. H. Marghny
%A Ahmed I. Taloba
%T Outlier Detection using Improved Genetic K-means
%J International Journal of Computer Applications
%@ 0975-8887
%V 28
%N 11
%P 33-36
%D 2011
%I Foundation of Computer Science (FCS), NY, USA

The outlier detection problem in some cases is similar to the classification problem. For example, the main concern of clustering-based outlier detection algorithms is to find clusters and outliers, which are often regarded as noise that should be removed in order to make more reliable clustering. In this article, we present an algorithm that provides outlier detection and data clustering simultaneously. The algorithmimprovesthe estimation of centroids of the generative distribution during the process of clustering and outlier discovery. The proposed algorithm consists of two stages. The first stage consists of improved genetic k-means algorithm (IGK) process, while the second stage iteratively removes the vectors which are far from their cluster centroids.

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Index Terms

Computer Science
Information Sciences


Outlier detection Genetic algorithms Clustering K-means algorithm Improved Genetic K-means (IGK)