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

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.