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Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering

Af: Laith Mohammad Qasim Abualigah Engelsk Hardback

Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering

Af: Laith Mohammad Qasim Abualigah Engelsk Hardback
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This book puts forward a new method for solving the text document (TD) clustering problem, which is established in two main stages: (i) A new feature selection method based on a particle swarm optimization algorithm with a novel weighting scheme is proposed, as well as a detailed dimension reduction technique, in order to obtain a new subset of more informative features with low-dimensional space. This new subset is subsequently used to improve the performance of the text clustering (TC) algorithm and reduce its computation time. The k-mean clustering algorithm is used to evaluate the effectiveness of the obtained subsets. (ii) Four krill herd algorithms (KHAs), namely, the (a) basic KHA, (b) modified KHA, (c) hybrid KHA, and (d) multi-objective hybrid KHA, are proposed to solve the TC problem; each algorithm represents an incremental improvement on its predecessor. For the evaluation process, seven benchmark text datasets are used with different characterizations and complexities.

Text document (TD) clustering is a new trend in text mining in which the TDs are separated into several coherent clusters, where all documents in the same cluster are similar. The findings presented here confirm that the proposed methods and algorithms delivered the best results in comparison with other, similar methods to be found in the literature.

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This book puts forward a new method for solving the text document (TD) clustering problem, which is established in two main stages: (i) A new feature selection method based on a particle swarm optimization algorithm with a novel weighting scheme is proposed, as well as a detailed dimension reduction technique, in order to obtain a new subset of more informative features with low-dimensional space. This new subset is subsequently used to improve the performance of the text clustering (TC) algorithm and reduce its computation time. The k-mean clustering algorithm is used to evaluate the effectiveness of the obtained subsets. (ii) Four krill herd algorithms (KHAs), namely, the (a) basic KHA, (b) modified KHA, (c) hybrid KHA, and (d) multi-objective hybrid KHA, are proposed to solve the TC problem; each algorithm represents an incremental improvement on its predecessor. For the evaluation process, seven benchmark text datasets are used with different characterizations and complexities.

Text document (TD) clustering is a new trend in text mining in which the TDs are separated into several coherent clusters, where all documents in the same cluster are similar. The findings presented here confirm that the proposed methods and algorithms delivered the best results in comparison with other, similar methods to be found in the literature.

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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 165
ISBN-13: 9783030106737
Indbinding: Hardback
Udgave:
ISBN-10: 303010673X
Kategori: Kunstig intelligens
Udg. Dato: 3 jan 2019
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Nature Switzerland AG
Oplagsdato: 3 jan 2019
Forfatter(e): Laith Mohammad Qasim Abualigah
Forfatter(e) Laith Mohammad Qasim Abualigah

Kategori Kunstig intelligens

ISBN-13 9783030106737

Sprog Engelsk

Indbinding Hardback

Sider 165

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 3 jan 2019

Oplagsdato 3 jan 2019

Forlag Springer Nature Switzerland AG