# Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization
## 1 Introduction  
Clustering text documents is an important field in the area of Natural Language Processing (NLP) as it simplifies the tedious process of categorizing specific documents among millions of resources, especially when metadata such as key phrases, titles, and labels are not available. Text clustering is valuable for different applications, including topic extraction, spam filtering, automatic document categorization, user reviews analysis, and fast information retrieval.  
The process of clustering text written in natural languages is complicated, especially for the Arabic language. One of the complications in Arabic is the language's morphological complexity. For instance, a word in Arabic can be written in several forms that might exceed ten forms [1]. Ambiguity is also another major complication in the Arabic language, which is caused by the richness and complexity of Arabic morphology [1,2]. There are various other factors in the Arabic language causing difficulty in text clustering. Among these factors are the different dialects for different regions. Texts from different regions may exhibit significant linguistic variations. Moreover, in the Arabic language, the ordering of words in a sentence provides quite different interpretations for that sentence [3,4].  
Several Arabic text clustering techniques have been proposed by researchers to encounter these challenges. Among the various techniques, it has been concluded that the K-Means clustering algorithm is the most widely applied, and that is due to its simplicityand efficiency in comparison with other clustering algorithms [2; 5; 6; 7]. However, the initiation process of K-Means weakens its accuracy results. The initiation starts with plotting the centers of the clusters randomly and then assigning documents to the nearest center. If the initiation process is inaccurate, then the clustering will be imprecise [8]. Researchers proposed the use of K-Means++, which is an improved algorithm for the initialization process of K-Means [9]. However, our experiments show that even with this smart initialization process, the accuracy of the clustering is low compared to other techniques. Researchers also proposed the use of other clustering techniques, such as Suffix Tree clustering [10] and SOM [11]. Suffix Tree clustering has a limitation of overlapping documents in different clusters [12], while SOM clustering techniques demonstrated high effectiveness in clustering text even with high-dimensional datasets [13; 14; 15; 16; 17].  
In this paper, we introduce a new optimized SOM clustering approach that utilizes Grey Wolf Optimization (GWO) [18] to enhance the clustering performance and accuracy of the traditional SOM clustering. To the best of our knowledge, the integration of SOM and the GWO algorithm is the first of its kind. Hence, we also investigate its efficiency and effectiveness. We evaluate our proposed approach using different clustering metrics, such as the F1-score, precision, recall, and accuracy. More specifically, the contributions of this paper are as follows:  
* A novel Arabic text clustering approach that is based on Self-Organizing Maps (SOM) and Grey Wolf Optimization (GWO).
* An extensive overview of the research that is related to our approach.
* An evaluation of our proposed approach that demonstrates its effectiveness and efficiency in comparison with other clustering techniques.
* A publicly available implementation of our proposed approach.  
The remainder of this paper is organized as follows. In Section 2, we give an overview of the research related to our approach. Section 3 provides background information on the components used in our approach. In Section 4, we describe the details of our proposed Arabic text clustering approach. We provide a detailed experimental evaluation in Section 5, and we conclude in Section 6.