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
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id	my-usm-ep.53371
record_format	uketd_dc
spelling	my-usm-ep.533712022-07-14T07:17:03Z Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction 2021-02 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 http://eprints.usm.my/53371/ http://eprints.usm.my/53371/1/AMMAR%20KAMAL%20MOUSA%20ABASI%20-%20TESIS.pdf%20cut.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer (School of Computer Sciences)
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
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
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
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
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer (School of Computer Sciences)
publishDate	2021
url	http://eprints.usm.my/53371/1/AMMAR%20KAMAL%20MOUSA%20ABASI%20-%20TESIS.pdf%20cut.pdf
_version_	1747822238337335296

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

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