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Reseach Article

An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study

by M. H. Marghny, Rasha M. Abd El-Aziz, Ahmed I. Taloba
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 34 - Number 6
Year of Publication: 2011
Authors: M. H. Marghny, Rasha M. Abd El-Aziz, Ahmed I. Taloba

M. H. Marghny, Rasha M. Abd El-Aziz, Ahmed I. Taloba . An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study. International Journal of Computer Applications. 34, 6 ( November 2011), 1-6. DOI=10.5120/4092-5420

@article{ 10.5120/4092-5420,
author = { M. H. Marghny, Rasha M. Abd El-Aziz, Ahmed I. Taloba },
title = { An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study },
journal = { International Journal of Computer Applications },
issue_date = { November 2011 },
volume = { 34 },
number = { 6 },
month = { November },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { },
doi = { 10.5120/4092-5420 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:20:16.781410+05:30
%A M. H. Marghny
%A Rasha M. Abd El-Aziz
%A Ahmed I. Taloba
%T An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study
%J International Journal of Computer Applications
%@ 0975-8887
%V 34
%N 6
%P 1-6
%D 2011
%I Foundation of Computer Science (FCS), NY, USA

Clustering analysis plays an important role in scientific research and commercial application. K-means algorithm is a widely used partition method in clustering. However, it is known that the K-means algorithm may get stuck at suboptimal solutions, depending on the choice of the initial cluster centers. In this article, we propose a technique to handle large scale data, which can select initial clustering center purposefully using Genetic algorithms (GAs), reduce the sensitivity to isolated point, avoid dissevering big cluster, and overcome deflexion of data in some degree that caused by the disproportion in data partitioning owing to adoption of multi-sampling. We applied our method to some public datasets these show the advantages of the proposed approach for example Hepatitis C dataset that has been taken from the machine learning warehouse of University of California. Our aim is to evaluate hepatitis dataset. In order to evaluate this dataset we did some preprocessing operation, the reason to preprocessing is to summarize the data in the best and suitable way for our algorithm. Missing values of the instances are adjusted using local mean method.

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Index Terms

Computer Science
Information Sciences


Genetic Algorithms Clustering K-means algorithm Squared-error criterion Hepatitis-C Virus (HCV)