Arabic Clustering Through Advanced Stemming and WordNet-Based Extraction for Water Cycle Cluster

Deema Mohammed Alsekait, Jaffar Atwan, Qusay Bsoul, Sharaf Alzoubi, Hanaa Fathi, Malik Jawarneh, Abeer Saber and Diaa Salama AbdElminaam
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Deema Mohammed Alsekait: Department of Computer Science and Information Technology, Applied College, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
Jaffar Atwan: Department of Computer Information System, Prince Abdullah Bin Ghazi Faculty of ICT, Al~Balqa Applied University, Al-Salt, Jordan
Qusay Bsoul: Cybersecurity Department, College of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan
Sharaf Alzoubi: College of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan
Hanaa Fathi: Applied Science Research Center, Applied Science Private University, Amman, Jordan
Malik Jawarneh: College of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan
Abeer Saber: Benha Univerity, Egypt
Diaa Salama AbdElminaam: MEU Research Unit, Middle East University, Amman, Jordan & Jadara Research Center, Jadara University, Irbid, Jordan

International Journal of Data Warehousing and Mining (IJDWM), 2024, vol. 20, issue 1, 1-25

Abstract: Natural language processing represents human language in computational technique, which is to achieve the extraction of important words. The verbs and nouns found in the Arabic language are significantly pertinent in the process of differentiating each class label available for the purpose of machine learning, specifically in 'Arabic Clustering'. This paper implemented the extraction of verbs and nouns sourced from the Qur'an and text clustering for further evaluation by using two datasets. The limitations of conventional clusters were identified, such as k-means clustering on the initial centroids. Therefore, the current work incorporated a novel clustering optimisation technique known as the water cycle algorithm; when combined with k-means, the algorithm would select the optimal initial centroids. Consequently, the experiments revealed the proposed extraction technique to outperform other extraction methods when using an actual Qur'an dataset.

Date: 2024
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