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Text mining = classification, clustering, and applications /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Text mining/ Ashok Srivastava, Mehran Sahami.
Reminder of title:
classification, clustering, and applications /
other author:
Sahami, Mehran.
Published:
Boca Raton, FL :CRC Press, : 2009.,
Description:
1 online resource (xxx, 290 p.) :ill. :
Subject:
Data mining - Statistical methods. -
Online resource:
https://www.taylorfrancis.com/books/9780429191985
ISBN:
9781420059458 (electronic bk.)
Text mining = classification, clustering, and applications /
Text mining
classification, clustering, and applications /[electronic resource] :Ashok Srivastava, Mehran Sahami. - Boca Raton, FL :CRC Press,2009. - 1 online resource (xxx, 290 p.) :ill. - Chapman & Hall/CRC data mining and knowledge discovery series. - Chapman & Hall/CRC data mining and knowledge discovery series..
Includes bibliographical references (p. 275) and index.
Analysis of text patterns using kernel methods / Marco Turchi, Alessia Mammone, Nello Cristianini -- Detection of bias in media outlets with statistical learning methods / Blaz Fortuna, Carolina Galleguillos, Nello Cristianini -- Collective classification for text classification / Galileo Namata ... [et al.] -- Topic models / David M. Blei, John D. Lafferty -- Nonnegative matrix and tensor factorization for discussion tracking / Brett W. Bader, Michael W. Berry, Amy N. Langville -- Text clustering with mixture of von Mises-Fisher distributions / Arindam Banerjee ... [et al.] -- Constrained partitional clustering of text data: an overview / Sugato Basu, Ian Davidson -- Adaptive information filtering / Yi Zhang -- Utility-based information distillation / Yiming Yang, Abhimanyu Lad -- Text search-enhanced with types and entities / Soumen Chakrabarti ... [et al.].
An extension of data mining, text mining involves the extraction of information and knowledge from unstructured text. This constantly evolving field is increasingly used by major corporations, such as Google, Yahoo, and Microsoft. Featuring contributions from leading researchers in the field, this book provides a detailed overview of text mining theory, applications, and visualization. The theory section discusses text mining, information retrieval, latent semantic analysis, pagerank, latent Dirichlet allocation, and probabilistic relational models. In the section on text mining applications.
ISBN: 9781420059458 (electronic bk.)Subjects--Topical Terms:
559162
Data mining
--Statistical methods.Index Terms--Genre/Form:
554714
Electronic books.
LC Class. No.: QA76.9.D343 / T393 2009eb
Dewey Class. No.: 006.3/12
National Library of Medicine Call No.: QA 76.9.D343
Text mining = classification, clustering, and applications /
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Ashok Srivastava, Mehran Sahami.
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Analysis of text patterns using kernel methods / Marco Turchi, Alessia Mammone, Nello Cristianini -- Detection of bias in media outlets with statistical learning methods / Blaz Fortuna, Carolina Galleguillos, Nello Cristianini -- Collective classification for text classification / Galileo Namata ... [et al.] -- Topic models / David M. Blei, John D. Lafferty -- Nonnegative matrix and tensor factorization for discussion tracking / Brett W. Bader, Michael W. Berry, Amy N. Langville -- Text clustering with mixture of von Mises-Fisher distributions / Arindam Banerjee ... [et al.] -- Constrained partitional clustering of text data: an overview / Sugato Basu, Ian Davidson -- Adaptive information filtering / Yi Zhang -- Utility-based information distillation / Yiming Yang, Abhimanyu Lad -- Text search-enhanced with types and entities / Soumen Chakrabarti ... [et al.].
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An extension of data mining, text mining involves the extraction of information and knowledge from unstructured text. This constantly evolving field is increasingly used by major corporations, such as Google, Yahoo, and Microsoft. Featuring contributions from leading researchers in the field, this book provides a detailed overview of text mining theory, applications, and visualization. The theory section discusses text mining, information retrieval, latent semantic analysis, pagerank, latent Dirichlet allocation, and probabilistic relational models. In the section on text mining applications.
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https://www.taylorfrancis.com/books/9780429191985
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