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Dynamic Sub-Swarm Approach of PSO Algorithms for Text Document Clustering

Text document clustering is one of the data mining techniques used in many real-world applications such as information retrieval from IoT Sensors data, duplicate content detection, and document organization. Swarm intelligence (SI) algorithms are suitable for solving complex text document clustering...

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Autores principales: Selvaraj, Suganya, Choi, Eunmi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783986/
https://www.ncbi.nlm.nih.gov/pubmed/36560022
http://dx.doi.org/10.3390/s22249653
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author Selvaraj, Suganya
Choi, Eunmi
author_facet Selvaraj, Suganya
Choi, Eunmi
author_sort Selvaraj, Suganya
collection PubMed
description Text document clustering is one of the data mining techniques used in many real-world applications such as information retrieval from IoT Sensors data, duplicate content detection, and document organization. Swarm intelligence (SI) algorithms are suitable for solving complex text document clustering problems compared to traditional clustering algorithms. The previous studies show that in SI algorithms, particle swarm optimization (PSO) provides an effective solution to text document clustering problems. This PSO still needs to be improved to avoid the problems such as premature convergence to local optima. In this paper, an approach called dynamic sub-swarm of PSO (subswarm-PSO) is proposed to improve the results of PSO for text document clustering problems and avoid the local optimum by improving the global search capabilities of PSO. The results of this proposed approach were compared with the standard PSO algorithm and K-means algorithm. As for performance assurance, the evaluation metric purity is used with six benchmark data sets. The experimental results of this study show that our proposed subswarm-PSO algorithm performs best with high purity comparing the standard PSO and K-means traditional algorithms and also the execution time of subswarm-PSO comparatively takes a little less than the standard PSO algorithm.
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spelling pubmed-97839862022-12-24 Dynamic Sub-Swarm Approach of PSO Algorithms for Text Document Clustering Selvaraj, Suganya Choi, Eunmi Sensors (Basel) Article Text document clustering is one of the data mining techniques used in many real-world applications such as information retrieval from IoT Sensors data, duplicate content detection, and document organization. Swarm intelligence (SI) algorithms are suitable for solving complex text document clustering problems compared to traditional clustering algorithms. The previous studies show that in SI algorithms, particle swarm optimization (PSO) provides an effective solution to text document clustering problems. This PSO still needs to be improved to avoid the problems such as premature convergence to local optima. In this paper, an approach called dynamic sub-swarm of PSO (subswarm-PSO) is proposed to improve the results of PSO for text document clustering problems and avoid the local optimum by improving the global search capabilities of PSO. The results of this proposed approach were compared with the standard PSO algorithm and K-means algorithm. As for performance assurance, the evaluation metric purity is used with six benchmark data sets. The experimental results of this study show that our proposed subswarm-PSO algorithm performs best with high purity comparing the standard PSO and K-means traditional algorithms and also the execution time of subswarm-PSO comparatively takes a little less than the standard PSO algorithm. MDPI 2022-12-09 /pmc/articles/PMC9783986/ /pubmed/36560022 http://dx.doi.org/10.3390/s22249653 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Selvaraj, Suganya
Choi, Eunmi
Dynamic Sub-Swarm Approach of PSO Algorithms for Text Document Clustering
title Dynamic Sub-Swarm Approach of PSO Algorithms for Text Document Clustering
title_full Dynamic Sub-Swarm Approach of PSO Algorithms for Text Document Clustering
title_fullStr Dynamic Sub-Swarm Approach of PSO Algorithms for Text Document Clustering
title_full_unstemmed Dynamic Sub-Swarm Approach of PSO Algorithms for Text Document Clustering
title_short Dynamic Sub-Swarm Approach of PSO Algorithms for Text Document Clustering
title_sort dynamic sub-swarm approach of pso algorithms for text document clustering
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783986/
https://www.ncbi.nlm.nih.gov/pubmed/36560022
http://dx.doi.org/10.3390/s22249653
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