Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering

Laith Mohammad Qasim Abualigah (Autor)

Buch | Hardcover

XXVII, 165 Seiten

2019
Springer International Publishing (Verlag)
978-3-030-10673-7 (ISBN)

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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.

Chapter 1. Introduction.- Chapter 2. Krill Herd Algorithm.- Chapter 3. Literature Review.- Chapter 4. Proposed Methodology.- Chapter 5. Experimental Results.- Chapter 6. Conclusion and Future Work.- References.- List Of Publications

"The book is well written, with high-quality tables and graphs. Each chapter ends with a collection of references, including the most recent work in the area. The book should be very useful for scholars who want to study the general field of text document clustering. It is also a good reference for those who work in text document clustering and use genetic algorithms." (Xiannong Meng, ComputingReviews, May 10, 2019)

Erscheinungsdatum	05.01.2019
Reihe/Serie	Studies in Computational Intelligence
Zusatzinfo	XXVII, 165 p. 23 illus., 21 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	454 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Themenwelt	Technik
Schlagworte	clustering algorithms • Dimension Reduction Techniques • Hybrid KHA • KHA • Krill Herd Algorithm • Multi-Objective Hybrid KHA • Particle swarm optimization algorithm • Text Document Clustering
ISBN-10	3-030-10673-X / 303010673X
ISBN-13	978-3-030-10673-7 / 9783030106737
Zustand	Neuware