The Text Mining Handbook

Covers text mining and link detection algorithms, pre-processing techniques, knowledge representation, visualization and applications.

Autorentext
Dr Ronen Feldman is a Senior Lecturer in the Mathematics and Computer Science Department of Bar-Ilan University and Director of the Data and Text Mining Laboratory. Dr Feldman is co-founder, Chief Scientist and Chairman of the Board of Clearforest, Ltd., a leader in developing next generation text mining applications for corporate and government clients. He also recently served as an Adjunct Professor at New York University's Stern School of Business. A pioneer in the areas of machine learning, data mining, and unstructured data management, he has authored or co-authored more than 70 published articles and conference papers in these areas.

Klappentext
Text mining is a new and exciting area of computer science that tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. The Text Mining Handbook 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 applications.


Zusammenfassung
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.

Inhalt
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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