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New Genetic Gravitational Search approach for Data Clustering using K-Harmonic Means

by Anuradha D. Thakare, C. A. Dhote, Rohini S Hanchate
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 99 - Number 13
Year of Publication: 2014
Authors: Anuradha D. Thakare, C. A. Dhote, Rohini S Hanchate
10.5120/17431-7773

Anuradha D. Thakare, C. A. Dhote, Rohini S Hanchate . New Genetic Gravitational Search approach for Data Clustering using K-Harmonic Means. International Journal of Computer Applications. 99, 13 ( August 2014), 5-8. DOI=10.5120/17431-7773

@article{ 10.5120/17431-7773,
author = { Anuradha D. Thakare, C. A. Dhote, Rohini S Hanchate },
title = { New Genetic Gravitational Search approach for Data Clustering using K-Harmonic Means },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 99 },
number = { 13 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 5-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume99/number13/17431-7773/ },
doi = { 10.5120/17431-7773 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:28:05.754349+05:30
%A Anuradha D. Thakare
%A C. A. Dhote
%A Rohini S Hanchate
%T New Genetic Gravitational Search approach for Data Clustering using K-Harmonic Means
%J International Journal of Computer Applications
%@ 0975-8887
%V 99
%N 13
%P 5-8
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this article the new hybrid data clustering approach, Gravitational Genetic KHM, based on Genetic algorithm (GA), Gravitational Search Algorithm (GSA) and K-harmonic Means (KHM) is proposed. Data Clustering is used to group similar set of objects into set of disjoint classes, object in class are highly similar than the objects in other classes. Among various clustering methods, KHM is one of the most popular clustering techniques. KHM is applied widely and works well in many fields, but this method runs in local optima. In the proposed approach the merits of Genetic Algorithm are used to escape the KHM clustering from local optima and to overcome the slow convergence speed of GSA. This paper is presented as work-in-progress in which the work model is proposed and some intermediate results are discussed which in turn will be compared with existing hybrid algorithms. The results are tested on several datasets.

References
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Index Terms

Computer Science
Information Sciences

Keywords

K-Harmonic Means (KHM) Clustering Gravitational Search Algorithm (GSA) Genetic Algorithm (GA).

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