Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization

Abstract

Arabic text clustering is an essential topic in Arabic Natural Language Processing (ANLP). Its significance resides in various applications, such as document indexing, categorization, user review analysis, and others. After inspecting the current work on clustering Arabic text, it is observed that most researchers focus on applying K-Means clustering while hindering other clustering techniques. Our evaluation shows that K-Means has a weakness of inconsistent clustering results and weak clustering performance when the data dimensionality increases. Unlike K-Means clustering, Artificial Neural Networks (ANN) models such as Self-Organizing Maps (SOM) demonstrated higher accuracy and efficiency in clustering even with high dimensional datasets. In this paper, we introduce a new clustering model based on an optimization technique called Grey Wolf Optimization (GWO) used conjointly with SOM clustering to enhance its clustering performance and accuracy. The evaluation results of our proposed technique show an improvement in the effectiveness and efficiency in comparison with state-of-the-art approaches.

Keywords

• natural language processing
• text clustering
• Arabic text
• grey wolf optimization
• document categorization
• topic extraction