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Arabic Text Clustering Methods And Suggested Solutions For Theme-based Quran Clustering: Analysis Of Literature

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dc.contributor.authorQusay Bsoulen_US
dc.contributor.authorJaffar Atwanen_US
dc.contributor.authorRosalina Abdul Salamen_US
dc.contributor.authorMalik Jawarnehen_US
dc.date.accessioned2024-05-29T01:58:04Z
dc.date.available2024-05-29T01:58:04Z
dc.date.issued2021
dc.date.submitted2022-1-28
dc.descriptionVolume: 9 No:4 (page: 15-34)en_US
dc.description.abstractText 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.en_US
dc.identifier.citationBsoul, Q., Salam, R. A., Atwan, J., & Jawarneh, M. (2021). Arabic Text Clustering Methods and Suggested Solutions for Theme-Based Quran Clustering: Analysis of Literature. JOURNAL OF INFORMATION SCIENCE THEORY AND PRACTICE, 9(4), 15-34, https://doi.org/10.1633/JISTaP.2021.9.4.2en_US
dc.identifier.doihttps://doi.org/10.1633/JISTaP.2021.9.4.2
dc.identifier.epage34
dc.identifier.issn2287-9099
dc.identifier.issue4
dc.identifier.spage15
dc.identifier.urihttps://jistap.accesson.kr/v.9/4/15/10125
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/9974
dc.identifier.volume9
dc.language.isoen_USen_US
dc.publisherKorea Institute of Science and Technology Information (KISTI)en_US
dc.relation.ispartofThe Journal of Information Science Theory and Practice (JISTaP)en_US
dc.subjecttext mining , Arabic text clustering algorithms , terms extraction , un-supervised feature selection , optimal initial centroiden_US
dc.titleArabic Text Clustering Methods And Suggested Solutions For Theme-based Quran Clustering: Analysis Of Literatureen_US
dc.typeArticleen_US
dspace.entity.typePublication

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