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The Text Mining Handbook
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  • Page extent: 424 pages
  • Size: 253 x 177 mm
  • Weight: 0.95 kg

Library of Congress

  • Dewey number: 005.74
  • Dewey version: 22
  • LC Classification: QA76.9.D343 F45 2006
  • LC Subject headings:
    • Data mining--Handbooks, manuals, etc

Library of Congress Record

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 (ISBN-13: 9780521836579 | ISBN-10: 0521836573)

Manufactured on demand: supplied direct from the printer

$89.99 (P)

Text mining tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, this book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches. Finally, it explores current real-world, mission-critical applications of text mining and link detection in such varied fields as M&A business intelligence, genomics research and counter-terrorism activities.


1. Introduction to text mining; 2. Core text mining operations; 3. Text mining preprocessing techniques; 4. Categorization; 5. Clustering; 6. Information extraction; 7. Probabilistic models for Information extraction; 8. Preprocessing applications using probabilistic and hybrid approaches; 9. Presentation-layer considerations for browsing and query refinement; 10. Visualization approaches; 11. Link analysis; 12. Text mining applications; Appendix; Bibliography.


" the book. This book is definitely worth having in your book shelf as a handy reference."
L. Venkata Subramaniam IAPR Newsletter

"A good introduction to text mining written by leading experts in the field. The book is well written and addresses both the theory and practice of text mining, which makes it appealing for researchers and practitioners alike... Highly recommended to those who would like to start delving into the area of text mining without having any previous background in computational linguistics."
Rada Mihalcea, University of North Texas, for Computational Linguistics

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