The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data

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.

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.

About the Author :
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. Jim Sanger is a venture capitalist, applied technologist and recognized industry expert in the areas of commercial data solutions, Internet applications and IT security products. He is a partner at ABS Ventures, an independent venture firm founded in 1982 and originally associated with technology banking leader Alex Brown and Sons. Immediately before joining ABS Ventures, Mr Sanger was a Managing Director in the New York offices of DB Capital Venture Partners, the global venture capital arm of Europe's largest financial institution, Deutsche Bank. Before transferring to DB Capital in New York, Mr Sanger was Chief Technology Officer and Director of Tech Investment for Deutsche Bank's London-based corporate development and venturing group, as well as Vice President of Software Development for the E-business division of the Deutsche Bank's investment banking organization. Prior to his work at Deutsche Bank, Mr Sanger held a variety of senior IT positions at Barclays Bank and Bell Atlantic Corporation (now Verizon Communications). Mr Sanger has been a board member of several thought-leading technology companies, including Inxight Software, Gomez, Inc., and Clearforest, Inc.; he has also served as an official observer to the boards of AlphaBlox (acquired by IBM in 2004), Intralinks, and Imagine Software, and as a member of the Technical Advisory Board of Qualys, Inc. He has been a speaker at leading technology conferences, including Internet World, M-Commerce World, Euromoney Seminars, and Yankee Group-sponsored events. Mr Sanger received his BA, cum laude, from the University of Pennsylvania and attended postgraduate courses in Software Engineering and Information Technology at Oxford University and the University of Liverpool. Mr Sanger is a member of the IEEE and American Association for Artificial Intelligence (AAAI).

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