Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction
This study aims to propose a suitable TE approach, which provides a better overview of the text documents. To achieve this aim: First, A new feature selection method for TDC, that is, binary multi-verse optimizer algorithm (BMVO) is proposed to eliminate irrelevantly, redundant features and obtain...
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| Format: | Thesis |
| Language: | English |
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2021
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| Online Access: | http://eprints.usm.my/53371/1/AMMAR%20KAMAL%20MOUSA%20ABASI%20-%20TESIS.pdf%20cut.pdf |
| _version_ | 1825906868613021696 |
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| author | Abasi, Ammar Kamal Mousa |
| author_facet | Abasi, Ammar Kamal Mousa |
| author_sort | Abasi, Ammar Kamal Mousa |
| collection | USM |
| description | This study aims to propose a suitable TE approach, which provides a better overview of the text documents. To achieve this aim: First, A new feature selection method for TDC, that is, binary multi-verse optimizer algorithm (BMVO) is
proposed to eliminate irrelevantly, redundant features and obtain a new subset of more informative features. Second, three multi-verse optimizer algorithm (MVOs), namely,
basic MVO, modified MVO, hybrid MVO is proposed to solve the TDC problem; these algorithms are incremental improvements of the preceding versions. Third, a novel ensemble
method for an automatic TE from a collection of text document is proposed to extract the topics from the clustered documents |
| first_indexed | 2024-03-06T15:55:57Z |
| format | Thesis |
| id | usm.eprints-53371 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2024-03-06T15:55:57Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | usm.eprints-533712022-07-14T07:17:03Z http://eprints.usm.my/53371/ Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction Abasi, Ammar Kamal Mousa QA75.5-76.95 Electronic computers. Computer science This study aims to propose a suitable TE approach, which provides a better overview of the text documents. To achieve this aim: First, A new feature selection method for TDC, that is, binary multi-verse optimizer algorithm (BMVO) is proposed to eliminate irrelevantly, redundant features and obtain a new subset of more informative features. Second, three multi-verse optimizer algorithm (MVOs), namely, basic MVO, modified MVO, hybrid MVO is proposed to solve the TDC problem; these algorithms are incremental improvements of the preceding versions. Third, a novel ensemble method for an automatic TE from a collection of text document is proposed to extract the topics from the clustered documents 2021-02 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/53371/1/AMMAR%20KAMAL%20MOUSA%20ABASI%20-%20TESIS.pdf%20cut.pdf Abasi, Ammar Kamal Mousa (2021) Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Abasi, Ammar Kamal Mousa Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction |
| title | Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction |
| title_full | Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction |
| title_fullStr | Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction |
| title_full_unstemmed | Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction |
| title_short | Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction |
| title_sort | improved multi verse optimizer in text document clustering for topic extraction |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://eprints.usm.my/53371/1/AMMAR%20KAMAL%20MOUSA%20ABASI%20-%20TESIS.pdf%20cut.pdf |
| work_keys_str_mv | AT abasiammarkamalmousa improvedmultiverseoptimizerintextdocumentclusteringfortopicextraction |