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Data Mining for Business Applications

By Longbing Cao , Philip S. Yu , Chengqi Zhang , Huaifeng Zhang

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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 book bridges the gap between business expectations and research outputs.

Full Description

  • ISBN13: 978-0-3877-9419-8
  • 324 Pages
  • User Level: Science
  • Publication Date: October 3, 2008
  • Available eBook Formats: PDF
  • eBook Price: $119.00
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Full Description
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.
Table of Contents

Table of Contents

  1. Part I Domain Driven KDD Methodology: Introduction to Domain Driven Data Mining.
  2. Post
  3. processing Data Mining Models for Actionability.
  4. On Mining Maximal Pattern
  5. Based Clusters.
  6. Role of Human Intelligence in Domain Driven Data Mining.
  7. Ontology Mining for Personalized Search.
  8. Part II Novel KDD Domains & Techniques: Data Mining Applications in Social Security.
  9. Security Data Mining: A Survey Introducing Tamper
  10. Resistance.
  11. A Domain Driven Mining Algorithm on Gene Sequence Clustering.
  12. Domain Driven Tree Mining of Semi
  13. structured Mental Health Information.
  14. Text Mining for Real
  15. time Ontology Evolution.
  16. Microarray Data Mining: Selecting Trustworthy Genes with Gene Feature Ranking.
  17. Blog Data Mining for Cyber Security Threats.
  18. Blog Data Mining: The Predictive Power of Sentiments.
  19. Web Mining: Extracting Knowledge from the WorldWideWeb.
  20. DAG Mining for Code Compaction.
  21. A Framework for Context
  22. Aware Trajectory Data Mining.
  23. Census Data Mining for Land Use Classification.
  24. Visual Data Mining for Developing Competitive Strategies in Higher Education.
  25. Data Mining For Robust Flight Scheduling.
  26. Data Mining for Algorithmic Asset Management.
  27. References.
  28. Reviewer List.
  29. Index.
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