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Text documents clustering using modified multi-verse optimizer
A.K. Abasi, A.T. Khader, , S. Naim, M.A. Awadallah, O.A. Alomari
Published in Institute of Advanced Engineering and Science
2020
Volume: 10
   
Issue: 6
Pages: 6361 - 6369
Abstract
In 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.
About the journal
JournalInternational Journal of Electrical and Computer Engineering
PublisherInstitute of Advanced Engineering and Science
ISSN20888708