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Text Documents Clustering using Genetic Algorithm and Discrete Differential Evolution

by Yogesh Kumar Meena, Shashank, Vibhav Prakash Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 43 - Number 1
Year of Publication: 2012
Authors: Yogesh Kumar Meena, Shashank, Vibhav Prakash Singh
10.5120/6067-8221

Yogesh Kumar Meena, Shashank, Vibhav Prakash Singh . Text Documents Clustering using Genetic Algorithm and Discrete Differential Evolution. International Journal of Computer Applications. 43, 1 ( April 2012), 16-19. DOI=10.5120/6067-8221

@article{ 10.5120/6067-8221,
author = { Yogesh Kumar Meena, Shashank, Vibhav Prakash Singh },
title = { Text Documents Clustering using Genetic Algorithm and Discrete Differential Evolution },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 43 },
number = { 1 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 16-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume43/number1/6067-8221/ },
doi = { 10.5120/6067-8221 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:32:15.062675+05:30
%A Yogesh Kumar Meena
%A Shashank
%A Vibhav Prakash Singh
%T Text Documents Clustering using Genetic Algorithm and Discrete Differential Evolution
%J International Journal of Computer Applications
%@ 0975-8887
%V 43
%N 1
%P 16-19
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Clustering in data mining is a discovery process that groups a set of documents such that documents within a cluster have high similarity while documents in different clusters have low similarity. Existing clustering method like K-means is a popular method but its results are based on choice of cluster centers so it easily results in local optimization. Genetic Algorithm (GA) is an optimization method which can be applied for finding out the best cluster centers easily. But sometimes it takes more iteration for finding best cluster centers. In this paper, we use features of GA with the features of Discrete Differential Evolution (DDE) to solve text documents clustering problem. To test the efficiency of our algorithm we have taken sample database of Reuters-21578. From the experimental results, it is clear that our algorithm performs better than GA and DDE.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm Discrete Differential Evolution Document Clustering