# Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization
## 2 Related Work
### Self-Organizing Maps  
Researchers have applied the Self-Organizing Maps (SOM) clustering algorithm in several domains, such as speech recognition [36], medical imaging and analysis [37], classification of satellite images [38], and others. The following presents some applications of the SOM algorithm. [PERSON] et al. attempted to resolve the issue of the sudden disabling of electronic car batteries [39]. The authors used SOM to provide the battery's performance. SOM were used to cluster the characteristics of the battery, including the battery's capacity, temperature, voltage, lifespan, internal resistance, and self-discharge rate. Afterward, the result of the clustering provides the driver with different information about the battery and its usage. Also, it alerts the driver when the battery should be replaced. The authors in this study did not mention the accuracy rate of their clustering. However, they compared the K-Means clustering algorithm with SOM and concluded that the latter outperformed K-Means. [PERSON] et al. applied the SOM algorithm to analyze students' E-Learning activities [40]. SOM are applied to provide clusters for the E-Learning activities to investigate the relation between the students' activities regarding the E-Learning portal and their academic performance. The authors obtained a dataset from the Universiti Teknologi Malaysia (UTM) Moodle LMS log records. Then, SOM were applied to cluster students according to their E-Learning activities. In their evaluation, the authors observed a correlation between the students' performance and their E-Learning activities, showing that the students' performance is affected by the activities positively. SOM were also used by [PERSON] and [PERSON] to detect fake followers on Twitter [41]. The authors applied their model to a dataset of fake followers provided by the Institute of Informatics and Telematics of the Italian National Research Council. The dataset uses the accounts' related features to categorize the type of user, whether fake or real. The features used include the following count, followers count, favorites count, and others. The use of SOM in this study showed its effectiveness in detecting fake accounts. [PERSON] et al. used SOM to detect damaged lesions in the brain from the Magnetic Resonance Imaging (MRI) scan for Relapsing Remitting Multiple Sclerosis (RRMS) patients [42]. SOM are applied to cluster lesions based on the possessed damage. The authors used a dataset of 10 patients to perform their study. They concluded the effectiveness of analyzing the MRI scans through SOM automatically. Similarly, in the research by [PERSON] et al. [43], the authors used SOM clustering as a disease early diagnosis tool. The authors used SOM to allocate significant gene paths in the human body. Gene paths can aid in the early diagnosis of different diseases, such as diabetes, heart diseases, and others. The authors used a dataset provided by the Kyoto Encyclopedia of Genes and Genomes (KEGG). They also applied the K-Means clustering algorithm but concluded that SOM performed better than K-Means. To conclude, the discussed related work of SOM shows its applicability to various research areas. In addition, many researchers performed a comparison between SOM and