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http://dx.doi.org/10.1633/JISTaP.2021.9.4.2

Arabic Text Clustering Methods and Suggested Solutions for Theme-Based Quran Clustering: Analysis of Literature  

Bsoul, Qusay (Cybersecurity Department, Science and IT College, Irbid National University)
Abdul Salam, Rosalina (Faculty of Science and Technology, Universiti Sains Islam Malaysia)
Atwan, Jaffar (Prince Abdullah Bin Ghazi Faculty of ICT, AL-Balqa Applied University)
Jawarneh, Malik (Faculty for Computing Sciences, Gulf College)
Publication Information
Journal of Information Science Theory and Practice / v.9, no.4, 2021 , pp. 15-34 More about this Journal
Abstract
Text clustering is one of the most commonly used methods for detecting themes or types of documents. Text clustering is used in many fields, but its effectiveness is still not sufficient to be used for the understanding of Arabic text, especially with respect to terms extraction, unsupervised feature selection, and clustering algorithms. In most cases, terms extraction focuses on nouns. Clustering simplifies the understanding of an Arabic text like the text of the Quran; it is important not only for Muslims but for all people who want to know more about Islam. This paper discusses the complexity and limitations of Arabic text clustering in the Quran based on their themes. Unsupervised feature selection does not consider the relationships between the selected features. One weakness of clustering algorithms is that the selection of the optimal initial centroid still depends on chances and manual settings. Consequently, this paper reviews literature about the three major stages of Arabic clustering: terms extraction, unsupervised feature selection, and clustering. Six experiments were conducted to demonstrate previously un-discussed problems related to the metrics used for feature selection and clustering. Suggestions to improve clustering of the Quran based on themes are presented and discussed.
Keywords
text mining; Arabic text clustering algorithms; terms extraction; un-supervised feature selection; optimal initial centroid;
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