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Journal of Intelligent Systems

Editor-in-Chief: Fleyeh, Hasan

4 Issues per year


CiteScore 2016: 0.39

SCImago Journal Rank (SJR) 2016: 0.170
Source Normalized Impact per Paper (SNIP) 2016: 0.206

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2191-026X
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Volume 26, Issue 1 (Jan 2017)

Issues

Grey Wolf Algorithm-Based Clustering Technique

Vijay Kumar / Jitender Kumar Chhabra
  • Computer Engineering Department, National Institute of Technology, Kurukshetra, Haryana, India
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Dinesh Kumar
Published Online: 2016-02-04 | DOI: https://doi.org/10.1515/jisys-2014-0137

Abstract

The main problem of classical clustering technique is that it is easily trapped in the local optima. An attempt has been made to solve this problem by proposing the grey wolf algorithm (GWA)-based clustering technique, called GWA clustering (GWAC), through this paper. The search capability of GWA is used to search the optimal cluster centers in the given feature space. The agent representation is used to encode the centers of clusters. The proposed GWAC technique is tested on both artificial and real-life data sets and compared to six well-known metaheuristic-based clustering techniques. The computational results are encouraging and demonstrate that GWAC provides better values in terms of precision, recall, G-measure, and intracluster distances. GWAC is further applied for gene expression data set and its performance is compared to other techniques. Experimental results reveal the efficiency of the GWAC over other techniques.

Keywords: Grey wolf algorithm; data clustering; K-means; metaheuristics

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About the article

Corresponding author: Vijay Kumar, Computer Science and Engineering Department, Thapar University, Patiala, Punjab, India, e-mail:


Received: 2014-09-23

Published Online: 2016-02-04

Published in Print: 2017-01-01


Citation Information: Journal of Intelligent Systems, ISSN (Online) 2191-026X, ISSN (Print) 0334-1860, DOI: https://doi.org/10.1515/jisys-2014-0137.

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