Document clustering based on firefly algorithm
Document clustering is widely used in Information Retrieval however, existing clustering techniques suffer from local optima problem in determining the k number of clusters.Various efforts have been put to address such drawback and this includes the utilization of swarm-based algorithms such as part...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Science Publications
2015
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| Online Access: | https://repo.uum.edu.my/id/eprint/17282/1/jcssp.2015.453.465.pdf |
| _version_ | 1825803849546334208 |
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| author | Mohammed, Athraa Jasim Yusof, Yuhanis Husni, Husniza |
| author_facet | Mohammed, Athraa Jasim Yusof, Yuhanis Husni, Husniza |
| author_sort | Mohammed, Athraa Jasim |
| collection | UUM |
| description | Document clustering is widely used in Information Retrieval however, existing clustering techniques suffer from local optima problem in determining the k number of clusters.Various efforts have been put to address such drawback and this includes the utilization of swarm-based algorithms such as particle swarm optimization and Ant Colony Optimization.This study explores the adaptation of another swarm algorithm which is the Firefly Algorithm (FA) in text clustering.We present two variants of FA; Weight- based Firefly Algorithm (WFA) and Weight-based Firefly Algorithm II (WFAII).The difference between the two algorithms is that the WFAII, includes a more restricted condition in determining members of a cluster.The proposed FA methods are later evaluated using the 20Newsgroups dataset.Experimental results on the quality of clustering between the two FA variants are presented and are later compared against the one produced by particle swarm optimization, K-means and the hybrid of FA and -K-means. The obtained results demonstrated that the WFAII outperformed the WFA, PSO, K-means and FA-Kmeans. This result indicates that a better clustering can be obtained once the exploitation of a search solution is improved. |
| first_indexed | 2024-07-04T06:04:42Z |
| format | Article |
| id | uum-17282 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:04:42Z |
| publishDate | 2015 |
| publisher | Science Publications |
| record_format | eprints |
| spelling | uum-172822016-04-27T06:52:17Z https://repo.uum.edu.my/id/eprint/17282/ Document clustering based on firefly algorithm Mohammed, Athraa Jasim Yusof, Yuhanis Husni, Husniza QA75 Electronic computers. Computer science Document clustering is widely used in Information Retrieval however, existing clustering techniques suffer from local optima problem in determining the k number of clusters.Various efforts have been put to address such drawback and this includes the utilization of swarm-based algorithms such as particle swarm optimization and Ant Colony Optimization.This study explores the adaptation of another swarm algorithm which is the Firefly Algorithm (FA) in text clustering.We present two variants of FA; Weight- based Firefly Algorithm (WFA) and Weight-based Firefly Algorithm II (WFAII).The difference between the two algorithms is that the WFAII, includes a more restricted condition in determining members of a cluster.The proposed FA methods are later evaluated using the 20Newsgroups dataset.Experimental results on the quality of clustering between the two FA variants are presented and are later compared against the one produced by particle swarm optimization, K-means and the hybrid of FA and -K-means. The obtained results demonstrated that the WFAII outperformed the WFA, PSO, K-means and FA-Kmeans. This result indicates that a better clustering can be obtained once the exploitation of a search solution is improved. Science Publications 2015 Article PeerReviewed application/pdf en cc_by https://repo.uum.edu.my/id/eprint/17282/1/jcssp.2015.453.465.pdf Mohammed, Athraa Jasim and Yusof, Yuhanis and Husni, Husniza (2015) Document clustering based on firefly algorithm. Journal of Computer Science, 11 (3). pp. 453-465. ISSN 1549-3636 http://doi.org/10.3844/jcssp.2015.453.465 doi:10.3844/jcssp.2015.453.465 doi:10.3844/jcssp.2015.453.465 |
| spellingShingle | QA75 Electronic computers. Computer science Mohammed, Athraa Jasim Yusof, Yuhanis Husni, Husniza Document clustering based on firefly algorithm |
| title | Document clustering based on firefly algorithm |
| title_full | Document clustering based on firefly algorithm |
| title_fullStr | Document clustering based on firefly algorithm |
| title_full_unstemmed | Document clustering based on firefly algorithm |
| title_short | Document clustering based on firefly algorithm |
| title_sort | document clustering based on firefly algorithm |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/17282/1/jcssp.2015.453.465.pdf |
| work_keys_str_mv | AT mohammedathraajasim documentclusteringbasedonfireflyalgorithm AT yusofyuhanis documentclusteringbasedonfireflyalgorithm AT husnihusniza documentclusteringbasedonfireflyalgorithm |