article

An efficient approach for unsupervised fuzzy clustering based on grouping evolution strategies

Authors:

Ali Husseinzadeh Kashan,

Babak Rezaee, and

Somayyeh KarimiyanAuthors Info & Claims

Pattern Recognition, Volume 46, Issue 5

Pages 1240 - 1254

https://doi.org/10.1016/j.patcog.2012.11.010

Published: 01 May 2013 Publication History

Abstract

Fuzzy data clustering plays an important role in practical use and has become a prerequisite step for decision-making in fuzzy environment. In this paper we propose a new algorithm, called FuzzyGES for unsupervised fuzzy clustering based on adaptation of the recently proposed Grouping Evolution Strategy (GES). To adapt GES for fuzzy clustering we devise a fuzzy counterpart of the grouping mutation operator typically used in GES, and employ it in a two phase procedure to generate a new clustering solution. Unlike conventional clustering algorithms which should get the number of clusters as an input, FuzzyGES tries to determine the true number of clusters as well as providing the optimal cluster centroids after several iterations. The proposed approach is validated through using several data sets and results are compared with those of fuzzy c-means algorithm, particle swarm optimization algorithm (PSO), differential evolution (DE) and league championship algorithm (LCA). We also investigate the performance of FuzzyGES through using different cluster validity indices. Our results indicate that FuzzyGES is fast and provides acceptable results in terms of both determining the correct number of clusters and the accurate cluster centroids.

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Cited By

Kashan AKeshmiry MDahooie JAbbasi-Pooya A(2018)A simple yet effective grouping evolutionary strategy (GES) algorithm for scheduling parallel machinesNeural Computing and Applications10.1007/s00521-016-2789-330:6(1925-1938)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s00521-016-2789-3
Salcedo-Sanz SGarcía-Díaz PDel Ser JBilbao MPortilla-Figueras J(2016)A novel Grouping Coral Reefs Optimization algorithm for optimal mobile network deployment problems under electromagnetic pollution and capacity control criteriaExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.02.03255:C(388-402)Online publication date: 15-Aug-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.02.032
Scitovski RVidović IBajer D(2016)A new fast fuzzy partitioning algorithmExpert Systems with Applications: An International Journal10.1016/j.eswa.2015.12.03451:C(143-150)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.eswa.2015.12.034
Show More Cited By

Index Terms

An efficient approach for unsupervised fuzzy clustering based on grouping evolution strategies
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

Index terms have been assigned to the content through auto-classification.

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cover image Pattern Recognition

Pattern Recognition Volume 46, Issue 5

May, 2013

296 pages

ISSN:0031-3203

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Copyright © Elsevier Ltd © 2012.

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Elsevier Science Inc.

United States

Publication History

Published: 01 May 2013

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Cited By

Kashan AKeshmiry MDahooie JAbbasi-Pooya A(2018)A simple yet effective grouping evolutionary strategy (GES) algorithm for scheduling parallel machinesNeural Computing and Applications10.1007/s00521-016-2789-330:6(1925-1938)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s00521-016-2789-3
Salcedo-Sanz SGarcía-Díaz PDel Ser JBilbao MPortilla-Figueras J(2016)A novel Grouping Coral Reefs Optimization algorithm for optimal mobile network deployment problems under electromagnetic pollution and capacity control criteriaExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.02.03255:C(388-402)Online publication date: 15-Aug-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.02.032
Scitovski RVidović IBajer D(2016)A new fast fuzzy partitioning algorithmExpert Systems with Applications: An International Journal10.1016/j.eswa.2015.12.03451:C(143-150)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.eswa.2015.12.034
José-García AGómez-Flores W(2016)Automatic clustering using nature-inspired metaheuristicsApplied Soft Computing10.1016/j.asoc.2015.12.00141:C(192-213)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1016/j.asoc.2015.12.001
Zarinbal MFazel Zarandi MTurksen I(2015)Relative entropy collaborative fuzzy clustering methodPattern Recognition10.1016/j.patcog.2014.09.01848:3(933-940)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1016/j.patcog.2014.09.018
Abin ABeigy H(2015)Active constrained fuzzy clusteringPattern Recognition10.1016/j.patcog.2014.09.00848:3(953-967)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1016/j.patcog.2014.09.008
Yang MTian Y(2015)Bias-correction fuzzy clustering algorithmsInformation Sciences: an International Journal10.1016/j.ins.2015.03.006309:C(138-162)Online publication date: 10-Jul-2015
https://dl.acm.org/doi/10.1016/j.ins.2015.03.006
Lin PHuang PKuo CLai Y(2014)A size-insensitive integrity-based fuzzy c-means method for data clusteringPattern Recognition10.1016/j.patcog.2013.11.03147:5(2042-2056)Online publication date: 1-May-2014
https://dl.acm.org/doi/10.1016/j.patcog.2013.11.031

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