An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering <sup>†</sup>

Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work, we applied a cluster-based approach for solution s...

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Autores principales:	Daphne Teck Ching Lai, Yuji Sato
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	multiobjective evolutionary algorithm multiobjective genetic algorithm particle swarm optimization evolutionary algorithm data clustering Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/525406b3be0b4bc9acb294c118f8c8f2
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spelling	oai:doaj.org-article:525406b3be0b4bc9acb294c118f8c8f22021-11-25T16:13:28ZAn Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering <sup>†</sup>10.3390/a141103381999-4893https://doaj.org/article/525406b3be0b4bc9acb294c118f8c8f22021-11-01T00:00:00Zhttps://www.mdpi.com/1999-4893/14/11/338https://doaj.org/toc/1999-4893Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work, we applied a cluster-based approach for solution selection in a multiobjective evolutionary algorithm based on decomposition with bare bones particle swarm optimization for data clustering and investigated its clustering performance. In our previous work, we found that MOEA/D with BBPSO performed the best on 10 datasets. Here, we extend this work applying a cluster-based approach tested on 13 UCI datasets. We compared with six multiobjective evolutionary clustering algorithms from the existing literature and ten from our previous work. The proposed technique was found to perform well on datasets highly overlapping clusters, such as CMC and Sonar. So far, we found only one work that used cluster-based MOEA for clustering data, the hierarchical topology multiobjective clustering algorithm. All other cluster-based MOEA found were used to solve other problems that are not data clustering problems. By clustering Pareto solutions and evaluating new candidates against the found cluster representatives, local search is introduced in the solution selection process within the objective space, which can be effective on datasets with highly overlapping clusters. This is an added layer of search control in the objective space. The results are found to be promising, prompting different areas of future research which are discussed, including the study of its effects with an increasing number of clusters as well as with other objective functions.Daphne Teck Ching LaiYuji SatoMDPI AGarticlemultiobjective evolutionary algorithmmultiobjectivegenetic algorithmparticle swarm optimizationevolutionary algorithmdata clusteringIndustrial engineering. Management engineeringT55.4-60.8Electronic computers. Computer scienceQA75.5-76.95ENAlgorithms, Vol 14, Iss 338, p 338 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multiobjective evolutionary algorithm multiobjective genetic algorithm particle swarm optimization evolutionary algorithm data clustering Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
spellingShingle	multiobjective evolutionary algorithm multiobjective genetic algorithm particle swarm optimization evolutionary algorithm data clustering Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95 Daphne Teck Ching Lai Yuji Sato An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering <sup>†</sup>
description	Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work, we applied a cluster-based approach for solution selection in a multiobjective evolutionary algorithm based on decomposition with bare bones particle swarm optimization for data clustering and investigated its clustering performance. In our previous work, we found that MOEA/D with BBPSO performed the best on 10 datasets. Here, we extend this work applying a cluster-based approach tested on 13 UCI datasets. We compared with six multiobjective evolutionary clustering algorithms from the existing literature and ten from our previous work. The proposed technique was found to perform well on datasets highly overlapping clusters, such as CMC and Sonar. So far, we found only one work that used cluster-based MOEA for clustering data, the hierarchical topology multiobjective clustering algorithm. All other cluster-based MOEA found were used to solve other problems that are not data clustering problems. By clustering Pareto solutions and evaluating new candidates against the found cluster representatives, local search is introduced in the solution selection process within the objective space, which can be effective on datasets with highly overlapping clusters. This is an added layer of search control in the objective space. The results are found to be promising, prompting different areas of future research which are discussed, including the study of its effects with an increasing number of clusters as well as with other objective functions.
format	article
author	Daphne Teck Ching Lai Yuji Sato
author_facet	Daphne Teck Ching Lai Yuji Sato
author_sort	Daphne Teck Ching Lai
title	An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering <sup>†</sup>
title_short	An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering <sup>†</sup>
title_full	An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering <sup>†</sup>
title_fullStr	An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering <sup>†</sup>
title_full_unstemmed	An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering <sup>†</sup>
title_sort	empirical study of cluster-based moea/d bare bones pso for data clustering <sup>†</sup>
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/525406b3be0b4bc9acb294c118f8c8f2
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An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering <sup>†</sup>

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