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Titlebook: Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering; Laith Mohammad Qasim Abualigah Book 2019 Springer Nature

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書(shū)目名稱Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering
編輯Laith Mohammad Qasim Abualigah
視頻videohttp://file.papertrans.cn/342/341564/341564.mp4
概述Presents a new method for solving the text document clustering problem and demonstrates that it can outperform other comparable methods.Covers the main text clustering preprocessing steps and the meta
叢書(shū)名稱Studies in Computational Intelligence
圖書(shū)封面Titlebook: Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering;  Laith Mohammad Qasim Abualigah Book 2019 Springer Nature
描述.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 her
出版日期Book 2019
關(guān)鍵詞Krill Herd Algorithm; KHA; Text Document Clustering; Dimension Reduction Techniques; Clustering Algorith
版次1
doihttps://doi.org/10.1007/978-3-030-10674-4
isbn_ebook978-3-030-10674-4Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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