Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction

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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Main Author: Abasi, Ammar Kamal Mousa
Format: Thesis
Language:English
Published: 2021
Subjects:
QA75.5-76.95 Electronic computers. Computer science
Online Access:http://eprints.usm.my/53371/1/AMMAR%20KAMAL%20MOUSA%20ABASI%20-%20TESIS.pdf%20cut.pdf
http://eprints.usm.my/53371/
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Institution: Universiti Sains Malaysia
Language: English
id my.usm.eprints.53371
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spelling my.usm.eprints.53371 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.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic QA75.5-76.95 Electronic computers. Computer science
spellingShingle QA75.5-76.95 Electronic computers. Computer science
Abasi, Ammar Kamal Mousa
Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction
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
format Thesis
author Abasi, Ammar Kamal Mousa
author_facet Abasi, Ammar Kamal Mousa
author_sort Abasi, Ammar Kamal Mousa
title 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_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_sort improved multi-verse optimizer in text document clustering for topic extraction
publishDate 2021
url http://eprints.usm.my/53371/1/AMMAR%20KAMAL%20MOUSA%20ABASI%20-%20TESIS.pdf%20cut.pdf
http://eprints.usm.my/53371/
_version_ 1738511195467415552

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