Ronen Feldman

Text Mining Handbook (eBook, ePUB)

Advanced Approaches in Analyzing Unstructured Data

• Format: ePub

Produktbeschreibung

Text mining is a new and exciting area of computer science research 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. Similarly, link detection - a rapidly evolving approach to the analysis of text that shares and builds upon many of the key elements of text mining - also provides new tools for people to better leverage their burgeoning textual data resources. 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. Finally, the book 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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 11. Dezember 2006
• Englisch
• ISBN-13: 9781139814065
• Artikelnr.: 53006878

Autorenporträt

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.

Inhaltsangabe

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.