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

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dc.contributor.authorAbasi, Ammar Kamal Mousa
dc.date.accessioned2022-07-14T07:11:55Z
dc.date.available2022-07-14T07:11:55Z
dc.date.issued2021-02
dc.description.abstractThis 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.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/15559
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectImproved Multi-Verse Optimizeren_US
dc.subjectText Document Clusteringen_US
dc.subjectTopic Extractionen_US
dc.titleImproved Multi-Verse Optimizer In Text Document Clustering For Topic Extractionen_US
dc.typeThesisen_US
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Pusat Pengajian Sains Komputer - Tesis