東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Document clustering." ]

切換: 標籤 | MARC模式 | ISBD

Feature selection and enhanced krill...

Abualigah, Laith Mohammad Qasim.

FindBook

Google Book

Amazon

博客來

Feature selection and enhanced krill herd algorithm for text document clustering

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Feature selection and enhanced krill herd algorithm for text document clustering/ by Laith Mohammad Qasim Abualigah.
作者:	Abualigah, Laith Mohammad Qasim.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xxvii, 165 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Document clustering. -
電子資源:	https://doi.org/10.1007/978-3-030-10674-4
ISBN:	9783030106744

Feature selection and enhanced krill herd algorithm for text document clustering
Abualigah, Laith Mohammad Qasim.

Feature selection and enhanced krill herd algorithm for text document clustering[electronic resource] /by Laith Mohammad Qasim Abualigah. - Cham :Springer International Publishing :2019. - xxvii, 165 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.8161860-949X ;. - Studies in computational intelligence ;v.816..

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.

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.

ISBN: 9783030106744

Standard No.: 10.1007/978-3-030-10674-4doiSubjects--Topical Terms:

934961
Document clustering.

LC Class. No.: QA278.55 / .A28 2019

Dewey Class. No.: 519.53

Feature selection and enhanced krill herd algorithm for text document clustering
LDR:02692nmm a2200337 a 4500 001 2178822
003 DE-He213
005 20190705115924.0
006 m d
007 cr nn 008maaau
008 191122s2019 gw s 0 eng d
020 $a 9783030106744 $q (electronic bk.)
020 $a 9783030106737 $q (paper)
024 7 $a 10.1007/978-3-030-10674-4 $2 doi
035 $a 978-3-030-10674-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA278.55 $b .A28 2019
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 519.53 $2 23
090 $a QA278.55 $b .A165 2019
100 1 $a Abualigah, Laith Mohammad Qasim. $3 3383367
245 1 0 $a Feature selection and enhanced krill herd algorithm for text document clustering $h [electronic resource] / $c by Laith Mohammad Qasim Abualigah.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xxvii, 165 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.816
505 0 $a 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.
520 $a 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.
650 0 $a Document clustering. $3 934961
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Studies in computational intelligence ; $v v.816. $3 3383368
856 4 0 $u https://doi.org/10.1007/978-3-030-10674-4
950 $a Intelligent Technologies and Robotics (Springer-42732)