文章基本信息

标题：Text documents clustering using modified multi-verse optimizer
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作者：Ammar Kamal Abasi ; Ahamad Tajudin Khader ; Mohammed Azmi Al-Betar 等
期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2020
卷号：10
期号：6
页码：6361-6369
DOI：10.11591/ijece.v10i6.pp6361-6369
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (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.
关键词：Test Document Clustering;Multi-Verse Optimizer;Optimization;Swarm Intelligence