# Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization
## 2 Related Work
### Arabic Text Clustering  
[PERSON] and [PERSON] demonstrated the issue of data mining and data with high dimensions [19]. To overcome the addressed problem, the authors applied modeling techniques to the documents before clustering them. The authors used the Modern Standard Arabic (MSA) dataset [20], which has several versions with different preprocessed articles. The outcome of this study showed that normalized data provided better quality in clustering than unnormalized ones. With normalization, the purity of their clusters was 0.933, and the F1-score was 0.8732. Similar to [PERSON] and [PERSON], [PERSON] et al. used K-Means to cluster an Arabic dataset corpus which contains 20 documents related to news and short anecdotes [21]. The highest clustering scores for the precision, recall, and F1-measure were 98%, 88%, and 93%, respectively. [PERSON] and [PERSON] also addressed the problem of the high dimensionality of documents by minimizing the dimensionality of documents using the Term Frequency (TF), Inverse Document Frequency (IDF), and Term Frequency-Inverse Document Frequency (TF-IDF) feature selection approaches [22]. Following that, K-Means and K-Medoids were used for the clustering. The authors implemented their experiment on a 300-document corpus they built. The authors reported that K-Medoids provided more accurate results than K-Means; the first scored 60%, 78%, and 67% for the precision, recall, and F1-measure, respectively, while the second scored 80%, 83%, and 81%, respectively. Another group of researchers used K-Means clustering along with the TF-IDF and Binary Term Occurrence (BTO) feature selection approaches [23]. The authors used a dataset that contains 1121 Arabic tweets. The outcome of their work showed that the BTO feature selection approach outperformed the TF-IDF. The literature for clustering Arabic text using K-Means shows high variation in the performance scores for clustering Arabic text, which could be attributed to the instability and inconsistency of the K-Means clustering algorithm. To overcome the limitations of the K-Means random initiation of cluster centroids, researchers used PSO-optimized K-Means to cluster Arabic text [24; 25; 26]. The use of Particle Swarm Optimization (PSO) contributes to selecting the initial seeds of K-Means. A group of researchers implemented their algorithm for the purpose of Quran verses theme clustering [24], whereas another group [25; 26] used three different datasets, named BBC, CNN, and OSAC [27]. The outcome of these research papers demonstrated the effectiveness of applying optimization methods for enhancing the accuracy of the clustering models used. Another work on clustering Arabic documents was based on the sentiment orientation and context of words in the data corpus [5]. The authors used the Brown clustering algorithm on user reviews of several topics, such as news, movies, and restaurants. The data in this research were collected from several sources [28; 29; 30; 31]. The evaluation results of this approach showed that the subjectivity and polarity of the clustering documents provided rates of 96% and 85%, respectively. The evaluation results indicated that the number of clusters also affects the accuracy rates, showing that fewer clusters provide better results. In another work [2], the authors used a combination of Markov Clustering, Fuzzy-C-Means, and Deep Belief Neural Networks (DBN) in an attempt to cluster Arabic documents. Two datasets were used in this study; the first was acquired from the Al-Jazeera news website with 10,000 documents and the second from a Saudi Press Agency [32] with 6000 documents. The clustering precision, recall, and F1-measure resulted in 91.2%, 90.9%, and 91.02%, respectively. The model that was used was highly impacted by the feature selection of the root words leading to imprecise clustering results. [PERSON] and [PERSON] [11] used Expectation-Maximization (EM), SOM, and K-Means algorithms to cluster Arabic documents.