Description

Book Synopsis
Presents a comprehensive discussion of the state-of-the-art in text mining and link detection. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, the book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches, ending with real-world, mission-critical applications.

Trade Review
' … buy the book. This book is definitely worth having in your book shelf as a handy reference.' IAPR Newsletter

Table of Contents
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 Text Mining Handbook

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    A Hardback by Ronen Feldman, James Sanger

    £74.99

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    Order before 4pm today for delivery by Tue 15 Sep 2026.

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      Book details

      Publisher Cambridge University Press
      Published 12 November 2006
      ISBN-13 9780521836579
      978-0521836579
      ISBN-10 0521836573
      Also in:
      Data mining Machine learning

      Description

      Book Synopsis
      Presents a comprehensive discussion of the state-of-the-art in text mining and link detection. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, the book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches, ending with real-world, mission-critical applications.

      Trade Review
      ' … buy the book. This book is definitely worth having in your book shelf as a handy reference.' IAPR Newsletter

      Table of Contents
      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.

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