Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering

Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering

Author: Laith Mohammad Qasim Abualigah

Publisher: Springer

ISBN: 9783030106744

Category: Technology & Engineering

Page: 165

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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.
Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering
Language: en
Pages: 165
Authors: Laith Mohammad Qasim Abualigah
Categories: Technology & Engineering
Type: BOOK - Published: 2018-12-18 - Publisher: Springer

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,
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Language: en
Pages: 200
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Categories: Computers
Type: BOOK - Published: 2020-06-02 - Publisher: John Wiley & Sons

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Pages: 765
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Language: en
Pages: 347
Authors: Seyedali Mirjalili
Categories: Computers
Type: BOOK - Published: 2022-09-20 - Publisher: CRC Press

Moth-Flame Optimization algorithm is an emerging meta-heuristic and has been widely used in both science and industry. Solving optimization problem using this algorithm requires addressing a number of challenges, including multiple objectives, constraints, binary decision variables, large-scale search space, dynamic objective function, and noisy parameters. Handbook of Moth-Flame Optimization Algorithm: