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Data Mining for Business Applications

  • Book
  • © 2009

Overview

Editors:
  1. Longbing Cao

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

  2. Philip S. Yu

    1. Department of Computer Science, University of Illinois at Chicago, Chicago

  3. Chengqi Zhang

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

  4. Huaifeng Zhang

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

  • 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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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.

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Table of contents (20 chapters)

  1. Front Matter

    Pages i-xix
    Download chapter PDF

  2. Domain Driven KDD Methodology

    1. Introduction to Domain Driven Data Mining

      • Cao Longbing
      Pages 3-10

    2. Post-processing Data Mining Models for Actionability

      • Qiang Yang
      Pages 11-30

    3. On Mining Maximal Pattern-Based Clusters

      • Jian Pei, Xiaoling Zhang, Moonjung Cho, Haixun Wang, Philip S. Yu
      Pages 31-52

    4. Role of Human Intelligence in Domain Driven Data Mining

      • Sumana Sharma, Kweku-Muata Osei-Bryson
      Pages 53-61

    5. Ontology Mining for Personalized Search

      • Yuefeng Li, Xiaohui Tao
      Pages 63-78

  3. Novel KDD Domains & Techniques

    1. Data Mining Applications in Social Security

      • Yanchang Zhao, Huaifeng Zhang, Longbing Cao, Huaifeng Zhang, Hans Bohlscheid, Yuming Ou et al.
      Pages 81-96

    2. Security Data Mining: A Survey Introducing Tamper-Resistance

      • Clifton Phua, Mafruz Ashrafi
      Pages 97-110

    3. A Domain Driven Mining Algorithm on Gene Sequence Clustering

      • Yun Xiong, Ming Chen, Yangyong Zhu
      Pages 111-126

    4. Domain Driven Tree Mining of Semi-structured Mental Health Information

      • Maja Hadzic, Fedja Hadzic, Tharam S. Dillon
      Pages 127-141

    5. Text Mining for Real-time Ontology Evolution

      • Jackei H. K. Wong, Tharam S. Dillon, Allan K. Y. Wong, Wilfred W. K. Lin
      Pages 143-157

    6. Microarray Data Mining: Selecting Trustworthy Genes with Gene Feature Ranking

      • Ubaudi A. Franco, J. Kennedy Paul, R. Catchpoole Daniel, Guo Dachuan, J. Simoff Simeon
      Pages 159-168

    7. Blog Data Mining for Cyber Security Threats

      • Flora S. Tsai, Kap Luk Chan
      Pages 169-182

    8. Blog Data Mining: The Predictive Power of Sentiments

      • Yang Liu, Xiaohui Yu, Xiangji Huang, Aijun An
      Pages 183-195

    9. Web Mining: Extracting Knowledge from the World Wide Web

      • Zhongzhi Shi, Huifang Ma, Qing He
      Pages 197-208

    10. DAG Mining for Code Compaction

      • T. Werth, M. Wörlein, A. Dreweke, I. Fischer, M. Philippsen
      Pages 209-223

    11. A Framework for Context-Aware Trajectory

      • Vania Bogorny, Monica Wachowicz
      Pages 225-239

    12. Census Data Mining for Land Use Classification

      • E. Roma Neto, D. S. Hamburger
      Pages 241-251

    13. Visual Data Mining for Developing Competitive Strategies in Higher Education

      • Gürdal Ertek
      Pages 253-266

    14. Data Mining For Robust Flight Scheduling

      • Ira Assent, Ralph Krieger, Petra Welter, Jörg Herbers, Thomas Seidl
      Pages 267-282
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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

  • 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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