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Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)

Clustering, an unsupervised method of grouping sets of data, is used as a solution technique in various fields to divide and restructure data to become more significant and transform them into more useful information. Generally, clustering is difficult and complex phenomenon, where the appropriate numb...

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Main Authors: Rashed, Alwatben Batoul, Hamdan, Hazlina, Mohd Sharef, Nurfadhlina, Sulaiman, Md Nasir, Yaakob, Razali, Abubakar, Mansir
Format: Article
Language:English
Published: Universitas Ahmad Dahlan 2020
Online Access:http://psasir.upm.edu.my/id/eprint/86855/1/Multi%20objective%20clustering%20algorithm%20using%20particle%20swarm.pdf
http://psasir.upm.edu.my/id/eprint/86855/
https://ijain.org/index.php/IJAIN/article/view/366
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spelling my.upm.eprints.868552021-11-22T02:22:21Z http://psasir.upm.edu.my/id/eprint/86855/ Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD) Rashed, Alwatben Batoul Hamdan, Hazlina Mohd Sharef, Nurfadhlina Sulaiman, Md Nasir Yaakob, Razali Abubakar, Mansir Clustering, an unsupervised method of grouping sets of data, is used as a solution technique in various fields to divide and restructure data to become more significant and transform them into more useful information. Generally, clustering is difficult and complex phenomenon, where the appropriate numbers of clusters are always unknown, comes with a large number of potential solutions, and as well the datasets are unsupervised. These problems can be addressed by the Multi-Objective Particle Swarm Optimization (MOPSO) approach, which is commonly used in addressing optimization problems. However, MOPSO algorithm produces a group of non-dominated solutions which make the selection of an “appropriate” Pareto optimal or non-dominated solution more difficult. According to the literature, crowding distance is one of the most efficient algorithms that was developed based on density measures to treat the problem of selection mechanism for archive updates. In an attempt to address this problem, the clustering-based method that utilizes crowding distance (CD) technique to balance the optimality of the objectives in Pareto optimal solution search is proposed. The approach is based on the dominance concept and crowding distances mechanism to guarantee survival of the best solution. Furthermore, we used the Pareto dominance concept after calculating the value of crowding degree for each solution. The proposed method was evaluated against five clustering approaches that have succeeded in optimization that comprises of K-means Clustering, MCPSO, IMCPSO, Spectral clustering, Birch, and average-link algorithms. The results of the evaluation show that the proposed approach exemplified the state-of-the-art method with significant differences in most of the datasets tested. Universitas Ahmad Dahlan 2020-03 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/86855/1/Multi%20objective%20clustering%20algorithm%20using%20particle%20swarm.pdf Rashed, Alwatben Batoul and Hamdan, Hazlina and Mohd Sharef, Nurfadhlina and Sulaiman, Md Nasir and Yaakob, Razali and Abubakar, Mansir (2020) Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD). International Journal of Advances in Intelligent Informatics, 6 (1). 72 - 81. ISSN 2442-6571; ESSN: 2548-3161 https://ijain.org/index.php/IJAIN/article/view/366 10.26555/ijain.v6i1.366
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Clustering, an unsupervised method of grouping sets of data, is used as a solution technique in various fields to divide and restructure data to become more significant and transform them into more useful information. Generally, clustering is difficult and complex phenomenon, where the appropriate numbers of clusters are always unknown, comes with a large number of potential solutions, and as well the datasets are unsupervised. These problems can be addressed by the Multi-Objective Particle Swarm Optimization (MOPSO) approach, which is commonly used in addressing optimization problems. However, MOPSO algorithm produces a group of non-dominated solutions which make the selection of an “appropriate” Pareto optimal or non-dominated solution more difficult. According to the literature, crowding distance is one of the most efficient algorithms that was developed based on density measures to treat the problem of selection mechanism for archive updates. In an attempt to address this problem, the clustering-based method that utilizes crowding distance (CD) technique to balance the optimality of the objectives in Pareto optimal solution search is proposed. The approach is based on the dominance concept and crowding distances mechanism to guarantee survival of the best solution. Furthermore, we used the Pareto dominance concept after calculating the value of crowding degree for each solution. The proposed method was evaluated against five clustering approaches that have succeeded in optimization that comprises of K-means Clustering, MCPSO, IMCPSO, Spectral clustering, Birch, and average-link algorithms. The results of the evaluation show that the proposed approach exemplified the state-of-the-art method with significant differences in most of the datasets tested.
format Article
author Rashed, Alwatben Batoul
Hamdan, Hazlina
Mohd Sharef, Nurfadhlina
Sulaiman, Md Nasir
Yaakob, Razali
Abubakar, Mansir
spellingShingle Rashed, Alwatben Batoul
Hamdan, Hazlina
Mohd Sharef, Nurfadhlina
Sulaiman, Md Nasir
Yaakob, Razali
Abubakar, Mansir
Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)
author_facet Rashed, Alwatben Batoul
Hamdan, Hazlina
Mohd Sharef, Nurfadhlina
Sulaiman, Md Nasir
Yaakob, Razali
Abubakar, Mansir
author_sort Rashed, Alwatben Batoul
title Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)
title_short Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)
title_full Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)
title_fullStr Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)
title_full_unstemmed Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)
title_sort multi-objective clustering algorithm using particle swarm optimization with crowding distance (mcpso-cd)
publisher Universitas Ahmad Dahlan
publishDate 2020
url http://psasir.upm.edu.my/id/eprint/86855/1/Multi%20objective%20clustering%20algorithm%20using%20particle%20swarm.pdf
http://psasir.upm.edu.my/id/eprint/86855/
https://ijain.org/index.php/IJAIN/article/view/366
_version_ 1718927718278496256
score 13.23648

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