Feature Selection And Enhanced Krill Herd Algorithm For Text Document Clustering
Text document (TD) clustering is a new trend in text mining in which the TDs are separated into several coherent clusters, where documents in the same cluster are similar. In this study, a new method for solving the TD clustering problem worked in the following two stages: (i) A new feature selecti...
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| Format: | Thesis |
| Language: | English |
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2018
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| Online Access: | http://eprints.usm.my/43662/ http://eprints.usm.my/43662/1/LAITH%20MOHAMMAD%20QASIM%20ABUALIGAH.pdf |
| _version_ | 1848879867083358208 |
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| author | Abualigah, Laith Mohammad Qasim |
| author_facet | Abualigah, Laith Mohammad Qasim |
| author_sort | Abualigah, Laith Mohammad Qasim |
| building | USM Institutional Repository |
| collection | Online Access |
| description | Text document (TD) clustering is a new trend in text mining in which the TDs are separated into several coherent clusters, where documents in the same cluster are
similar. In this study, a new method for solving the TD clustering problem worked in the following two stages: (i) A new feature selection method using particle swarm
optimization algorithm with a novel weighting scheme and a detailed dimension reduction technique are proposed to obtain a new subset of more informative features with low-dimensional space. |
| first_indexed | 2025-11-15T17:54:03Z |
| format | Thesis |
| id | usm-43662 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T17:54:03Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-436622019-04-12T05:24:51Z http://eprints.usm.my/43662/ Feature Selection And Enhanced Krill Herd Algorithm For Text Document Clustering Abualigah, Laith Mohammad Qasim QA75.5-76.95 Electronic computers. Computer science Text document (TD) clustering is a new trend in text mining in which the TDs are separated into several coherent clusters, where documents in the same cluster are similar. In this study, a new method for solving the TD clustering problem worked in the following two stages: (i) A new feature selection method using particle swarm optimization algorithm with a novel weighting scheme and a detailed dimension reduction technique are proposed to obtain a new subset of more informative features with low-dimensional space. 2018-03 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/43662/1/LAITH%20MOHAMMAD%20QASIM%20ABUALIGAH.pdf Abualigah, Laith Mohammad Qasim (2018) Feature Selection And Enhanced Krill Herd Algorithm For Text Document Clustering. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Abualigah, Laith Mohammad Qasim Feature Selection And Enhanced Krill Herd Algorithm For Text Document Clustering |
| title | Feature Selection And Enhanced Krill Herd Algorithm For Text Document Clustering |
| title_full | Feature Selection And Enhanced Krill Herd Algorithm For Text Document Clustering |
| title_fullStr | Feature Selection And Enhanced Krill Herd Algorithm For Text Document Clustering |
| title_full_unstemmed | Feature Selection And Enhanced Krill Herd Algorithm For Text Document Clustering |
| title_short | Feature Selection And Enhanced Krill Herd Algorithm For Text Document Clustering |
| title_sort | feature selection and enhanced krill herd algorithm for text document clustering |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://eprints.usm.my/43662/ http://eprints.usm.my/43662/1/LAITH%20MOHAMMAD%20QASIM%20ABUALIGAH.pdf |