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Text mining = concepts, implementati...

Jo, Taeho.

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Text mining = concepts, implementation, and big data challenge /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Text mining/ by Taeho Jo.
其他題名:	concepts, implementation, and big data challenge /
作者:	Jo, Taeho.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xv, 447 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Data mining. -
電子資源:	https://doi.org/10.1007/978-3-031-75976-5
ISBN:	9783031759765

Text mining = concepts, 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. 45..

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

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

562972
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Text mining = concepts, implementation, and big data challenge /
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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.
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