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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| المؤلف الرئيسي: | |
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| التنسيق: | أطروحة |
| اللغة: | English |
| منشور في: |
2018
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| الوصول للمادة أونلاين: | http://eprints.usm.my/43662/1/LAITH%20MOHAMMAD%20QASIM%20ABUALIGAH.pdf |
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my-usm-ep.436622019-04-12T05:24:51Z Feature Selection And Enhanced Krill Herd Algorithm For Text Document Clustering 2018-03 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 http://eprints.usm.my/43662/ http://eprints.usm.my/43662/1/LAITH%20MOHAMMAD%20QASIM%20ABUALIGAH.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer |
| 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 Abualigah, Laith Mohammad Qasim Feature Selection And Enhanced Krill Herd Algorithm For Text Document Clustering |
| 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. |
| format |
Thesis |
| qualification_name |
Doctor of Philosophy (PhD.) |
| qualification_level |
Doctorate |
| author |
Abualigah, Laith Mohammad Qasim |
| author_facet |
Abualigah, Laith Mohammad Qasim |
| author_sort |
Abualigah, Laith Mohammad Qasim |
| title |
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_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_sort |
feature selection and enhanced krill herd algorithm for text document clustering |
| granting_institution |
Universiti Sains Malaysia |
| granting_department |
Pusat Pengajian Sains Komputer |
| publishDate |
2018 |
| url |
http://eprints.usm.my/43662/1/LAITH%20MOHAMMAD%20QASIM%20ABUALIGAH.pdf |
| _version_ |
1747821257672359936 |