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Open AccessArticle

Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks

by 1 and 1,2,*
1
Department of Financial Information Security, Kookmin University, Seoul 02707, Korea
2
Department of Software, College of Computer Science, Kookmin University, Seoul 02707, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Gianvito Pio, Roberto Corizzo and Michelangelo Ceci
Sensors 2021, 21(9), 3196; https://doi.org/10.3390/s21093196
Received: 31 March 2021 / Revised: 27 April 2021 / Accepted: 29 April 2021 / Published: 4 May 2021
(This article belongs to the Special Issue Geo-Distributed Big Data Analytics in Sensor Networks)
Text document clustering refers to the unsupervised classification of textual documents into clusters based on content similarity and can be applied in applications such as search optimization and extracting hidden information from data generated by IoT sensors. Swarm intelligence (SI) algorithms use stochastic and heuristic principles that include simple and unintelligent individuals that follow some simple rules to accomplish very complex tasks. By mapping features of problems to parameters of SI algorithms, SI algorithms can achieve solutions in a flexible, robust, decentralized, and self-organized manner. Compared to traditional clustering algorithms, these solving mechanisms make swarm algorithms suitable for resolving complex document clustering problems. However, each SI algorithm shows a different performance based on its own strengths and weaknesses. In this paper, to find the best performing SI algorithm in text document clustering, we performed a comparative study for the PSO, bat, grey wolf optimization (GWO), and K-means algorithms using six data sets of various sizes, which were created from BBC Sport news and 20 newsgroups. Based on our experimental results, we discuss the features of a document clustering problem with the nature of SI algorithms and conclude that the PSO and GWO SI algorithms are better than K-means, and among those algorithms, the PSO performs best in terms of finding the optimal solution. View Full-Text
Keywords: swarm intelligence algorithms; text document clustering; artificial intelligence; data mining swarm intelligence algorithms; text document clustering; artificial intelligence; data mining
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MDPI and ACS Style

Selvaraj, S.; Choi, E. Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks. Sensors 2021, 21, 3196. https://doi.org/10.3390/s21093196

AMA Style

Selvaraj S, Choi E. Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks. Sensors. 2021; 21(9):3196. https://doi.org/10.3390/s21093196

Chicago/Turabian Style

Selvaraj, Suganya; Choi, Eunmi. 2021. "Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks" Sensors 21, no. 9: 3196. https://doi.org/10.3390/s21093196

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