Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization
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 m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/18/10168 |
| _version_ | 1797581461593784320 |
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| author | Souad Larabi-Marie-Sainte Mashael Bin Alamir Abdulmajeed Alameer |
| author_facet | Souad Larabi-Marie-Sainte Mashael Bin Alamir Abdulmajeed Alameer |
| author_sort | Souad Larabi-Marie-Sainte |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T23:05:00Z |
| format | Article |
| id | doaj.art-becca76398a344fb8ddb040ad88fda3e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:05:00Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-becca76398a344fb8ddb040ad88fda3e2023-11-19T09:23:57ZengMDPI AGApplied Sciences2076-34172023-09-0113181016810.3390/app131810168Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf OptimizationSouad Larabi-Marie-Sainte0Mashael Bin Alamir1Abdulmajeed Alameer2Computer Science Department, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi ArabiaGraduate Unit, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi ArabiaComputer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaArabic 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.https://www.mdpi.com/2076-3417/13/18/10168natural language processingtext clusteringArabic textgrey wolf optimizationdocument categorizationtopic extraction |
| spellingShingle | Souad Larabi-Marie-Sainte Mashael Bin Alamir Abdulmajeed Alameer Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization Applied Sciences natural language processing text clustering Arabic text grey wolf optimization document categorization topic extraction |
| title | Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization |
| title_full | Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization |
| title_fullStr | Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization |
| title_full_unstemmed | Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization |
| title_short | Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization |
| title_sort | arabic text clustering using self organizing maps and grey wolf optimization |
| topic | natural language processing text clustering Arabic text grey wolf optimization document categorization topic extraction |
| url | https://www.mdpi.com/2076-3417/13/18/10168 |
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