Editors:
- Presents knowledge, techniques and case studies to bridge the gap between business expectations and research outputs
- Explores new research issues in data mining, including trust, organizational and social factors
- Addresses recent applications in areas such as blog mining and social security mining
- Introduces techniques and methodologies evidenced and validated in real-life enterprise data mining
- Includes supplementary material: sn.pub/extras
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Table of contents (20 chapters)
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Front Matter
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Novel KDD Domains & Techniques
About this book
Data Mining for Business Applications presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The contributions mark a paradigm shift from “data-centered pattern mining” to “domain driven actionable knowledge discovery” for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in theory and practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future research and development in the dialogue between academia and business.
Reviews
From the reviews:
"This is a compendium of papers written by 58 authors from different countries--including six from the US. … present the full gamut of current research in the field of actionable knowledge discovery (AKD), as it applies to real-world problems. … the intended audience of this book clearly includes industry practitioners, as well. … The editors have culled a wide array of methodologies for and applications of data mining, from the cutting edge of research. This book provides … further the development of actionable systems." (R. Goldberg, ACM Computing Reviews, June, 2009)
Editors and Affiliations
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School of Software Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia
Longbing Cao, Huaifeng Zhang
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Department of Computer Science, University of Illinois at Chicago, Chicago
Philip S. Yu
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Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia
Chengqi Zhang
Bibliographic Information
Book Title: Data Mining for Business Applications
Editors: Longbing Cao, Philip S. Yu, Chengqi Zhang, Huaifeng Zhang
DOI: https://doi.org/10.1007/978-0-387-79420-4
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag US 2009
Hardcover ISBN: 978-0-387-79419-8Published: 09 October 2008
Softcover ISBN: 978-1-4419-4635-5Published: 04 November 2010
eBook ISBN: 978-0-387-79420-4Published: 03 October 2008
Edition Number: 1
Number of Pages: XX, 302
Topics: Data Mining and Knowledge Discovery, Business Strategy/Leadership, Information Storage and Retrieval, Artificial Intelligence, Artificial Intelligence, Models and Principles