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A Hybrid Clustering Technique Combining A PSO Algorithm with K-Means

by Kripa Shankar Bopche, Anurag Jain
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 137 - Number 1
Year of Publication: 2016
Authors: Kripa Shankar Bopche, Anurag Jain
10.5120/ijca2016908678

Kripa Shankar Bopche, Anurag Jain . A Hybrid Clustering Technique Combining A PSO Algorithm with K-Means. International Journal of Computer Applications. 137, 1 ( March 2016), 40-44. DOI=10.5120/ijca2016908678

@article{ 10.5120/ijca2016908678,
author = { Kripa Shankar Bopche, Anurag Jain },
title = { A Hybrid Clustering Technique Combining A PSO Algorithm with K-Means },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 137 },
number = { 1 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 40-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume137/number1/24243-2016908678/ },
doi = { 10.5120/ijca2016908678 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:37:13.147697+05:30
%A Kripa Shankar Bopche
%A Anurag Jain
%T A Hybrid Clustering Technique Combining A PSO Algorithm with K-Means
%J International Journal of Computer Applications
%@ 0975-8887
%V 137
%N 1
%P 40-44
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Particle Swarm Optimization (PSO) is an evolutionary computation technique. Separate adjustment to inertia weight and learning factors in PSO undermines the integrity and intelligent characteristic in the evolutionary process of particle swarm to some extent, thus it is not suitable for solving most complicated optimization problems. On the basis of previous researches, the aim of this study was to improve the computational efficiency of PSO and avoid premature convergence for multimodal, higher dimensional complicated optimization problems by considering the mutual influences of inertia weight and learning factors on the updates of particle’s velocities. A typical data analytical scenario is a multidimensional problem and data clustering can lead to multi spatial analysis. Cluster can be a result of various algorithms. In this paper PSO based k-means clustering is applied to generate clusters. And provide multimodal and higher dimensional complicated optimization problems, and can accelerate convergence speed, improve optimization quality effectively in comparison to the algorithms of PSO K-means.

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Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Clustering Evolutionary Algorithm K-means PSO

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