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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2021
|
| Subjects: | |
| Online Access: | http://eprints.usm.my/53371/1/AMMAR%20KAMAL%20MOUSA%20ABASI%20-%20TESIS.pdf%20cut.pdf http://eprints.usm.my/53371/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Sains Malaysia |
| Language: | English |
| Summary: | 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 |
|---|