Data Mining for Business Applications

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.

• Business Decision Making
• Business Intelligence
• Clustering
• Domain Driven Data Mining
• Domain Knowledge and Intelligence
• Enterprise Data Mining
• Knowledge Actionability
• Text Mining
• Web Mining
• classification
• data mining
• knowledge discovery

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)

• School of Software Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia

  Longbing Cao, Huaifeng Zhang

• Department of Computer Science, University of Illinois at Chicago, Chicago

  Philip S. Yu

• Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia

  Chengqi Zhang

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