Feature Selection And Enhanced Krill Herd Algorithm For Text Document Clustering

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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Main Author: Abualigah, Laith Mohammad Qasim
Format: Thesis
Language:English
Published: 2018
Subjects:
QA75.5-76.95 Electronic computers
Computer science
Online Access:http://eprints.usm.my/43662/1/LAITH%20MOHAMMAD%20QASIM%20ABUALIGAH.pdf
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id my-usm-ep.43662
record_format uketd_dc
spelling 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

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