國立高雄大學圖資館 |

Language: English

Back

Text miningconcepts, implementation, and big data challenge /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Text miningby Taeho Jo.
Reminder of title:	concepts, implementation, and big data challenge /
Author:	Jo, Taeho.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	xv, 447 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Data mining.
Online resource:	https://doi.org/10.1007/978-3-031-75976-5
ISBN:	9783031759765$q(electronic bk.)

Text miningconcepts, implementation, and big data challenge /
Jo, Taeho.

Text miningconcepts, implementation, and big data challenge /[electronic resource] :by Taeho Jo. - Second edition. - Cham :Springer Nature Switzerland :2024. - xv, 447 p. :ill. (some col.), digital ;24 cm. - Studies in big data,v. 452197-6511 ;. - Studies in big data ;v.1..

Part I: Foundation -- Introduction -- Text Indexing -- Text Encoding -- Text Association -- Part II: Text Categorization -- Text Categorization: Conceptual View -- Text Categorization: Approaches -- Text Categorization: Implementation -- Text Categorization: Evaluation -- Part III: Text Clustering -- Text Clustering: Conceptual View -- Text Clustering: Approaches -- Text Clustering: Implementation -- Text Clustering: Evaluation -- Part IV: Advanced Topics -- Text Summarization -- Text Segmentation -- Taxonomy Generation -- Dynamic Document Organization -- References -- Index.

This popular book, updated as a textbook for classroom use, discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections. The author provides the guidelines for implementing text mining systems in Java, as well as concepts and approaches. The book starts by providing detailed text preprocessing techniques and then goes on to provide concepts, the techniques, the implementation, and the evaluation of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text management. The book features exercises and code to help readers quickly learn and apply knowledge. Presents an array of updated techniques for preprocessing texts into structured forms, geared for classroom use; Outlines concepts of text categorization and clustering, their algorithms, and implementation guides; Includes advanced topics such as text summarization, text segmentation, topic mapping, and automatic text management.

ISBN: 9783031759765$q(electronic bk.)

Standard No.: 10.1007/978-3-031-75976-5doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Text miningconcepts, implementation, and big data challenge /
LDR:02720nmm a2200349 a 4500 001 673351
003 DE-He213
005 20241215115222.0
006 m d
007 cr nn 008maaau
008 250422s2024 sz s 0 eng d
020 $a 9783031759765$q(electronic bk.)
020 $a 9783031759758$q(paper)
024 7 $a 10.1007/978-3-031-75976-5 $2 doi
035 $a 978-3-031-75976-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b J62 2024
100 1 $a Jo, Taeho. $3 830094
245 1 0 $a Text mining $h [electronic resource] : $b concepts, implementation, and big data challenge / $c by Taeho Jo.
250 $a Second edition.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a xv, 447 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in big data, $x 2197-6511 ; $v v. 45
505 0 $a Part I: Foundation -- Introduction -- Text Indexing -- Text Encoding -- Text Association -- Part II: Text Categorization -- Text Categorization: Conceptual View -- Text Categorization: Approaches -- Text Categorization: Implementation -- Text Categorization: Evaluation -- Part III: Text Clustering -- Text Clustering: Conceptual View -- Text Clustering: Approaches -- Text Clustering: Implementation -- Text Clustering: Evaluation -- Part IV: Advanced Topics -- Text Summarization -- Text Segmentation -- Taxonomy Generation -- Dynamic Document Organization -- References -- Index.
520 $a This popular book, updated as a textbook for classroom use, discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections. The author provides the guidelines for implementing text mining systems in Java, as well as concepts and approaches. The book starts by providing detailed text preprocessing techniques and then goes on to provide concepts, the techniques, the implementation, and the evaluation of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text management. The book features exercises and code to help readers quickly learn and apply knowledge. Presents an array of updated techniques for preprocessing texts into structured forms, geared for classroom use; Outlines concepts of text categorization and clustering, their algorithms, and implementation guides; Includes advanced topics such as text summarization, text segmentation, topic mapping, and automatic text management.
650 0 $a Data mining. $3 184440
650 0 $a Big data. $3 609582
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Communications Engineering, Networks. $3 273745
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Information Storage and Retrieval. $3 274190
650 2 4 $a Data Analysis and Big Data. $3 913147
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Studies in big data ; $v v.1. $3 675357
856 4 0 $u https://doi.org/10.1007/978-3-031-75976-5
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)