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

Abasi, Ammar Kamal Mousa (2021) Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction. PhD thesis, Universiti Sains Malaysia.

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Abstract

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

Item Type:	Thesis (PhD)
Subjects:	Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Pusat Pengajian Sains Komputer (School of Computer Sciences) > Thesis
Depositing User:	Mr Aizat Asmawi Abdul Rahim
Date Deposited:	14 Jul 2022 07:17
Last Modified:	14 Jul 2022 07:17
URI:	http://eprints.usm.my/id/eprint/53371

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