Text documents clustering using modified multi-verse optimizer

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dc.authorscopusid57208488241
dc.authorscopusid24724794600
dc.authorscopusid57202908939
dc.authorscopusid55413206700
dc.authorscopusid6603390660
dc.authorscopusid55353965600
dc.contributor.authorAbasi, Ammar Kamal
dc.contributor.authorKhader, Ahamad Tajudin
dc.contributor.authorAl-Betar, Mohammed Azmi
dc.contributor.authorNaim, Syibrah
dc.contributor.authorAwadallah, Mohammed A.
dc.contributor.authorAlomari, Osama Ahmad
dc.date.accessioned2024-09-11T19:58:07Z
dc.date.available2024-09-11T19:58:07Z
dc.date.issued2020
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractIn this study, a multi-verse optimizer (MVO) is utilised for the text document clustering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems. For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the final results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce significant results in comparison with three well-established methods. Copyright c 2020 Insitute of Advanced Engineeering and Science. All rights reserved.en_US
dc.identifier.doi10.11591/IJECE.V10I6.PP6361-6369
dc.identifier.endpage6369en_US
dc.identifier.issn2088-8708en_US
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85092309466en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage6361en_US
dc.identifier.urihttps://doi.org/10.11591/IJECE.V10I6.PP6361-6369
dc.identifier.urihttps://hdl.handle.net/11363/8426
dc.identifier.volume10en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofInternational Journal of Electrical and Computer Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240903_Gen_US
dc.subjectMulti-verse optimizer; Optimization; Swarm intelligence; Test document clusteringen_US
dc.titleText documents clustering using modified multi-verse optimizeren_US
dc.typeArticleen_US

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