© 2009

Data Mining for Business Applications

  • Longbing Cao
  • Philip S. Yu
  • Chengqi Zhang
  • Huaifeng Zhang


  • 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


Table of contents

  1. Front Matter
    Pages i-xix
  2. Domain Driven KDD Methodology

    1. Jian Pei, Xiaoling Zhang, Moonjung Cho, Haixun Wang, Philip S. Yu
      Pages 31-52
    2. Sumana Sharma, Kweku-Muata Osei-Bryson
      Pages 53-61
    3. Yuefeng Li, Xiaohui Tao
      Pages 63-78
  3. Novel KDD Domains & Techniques

    1. Yanchang Zhao, Huaifeng Zhang, Longbing Cao, Huaifeng Zhang, Hans Bohlscheid, Yuming Ou et al.
      Pages 81-96
    2. Clifton Phua, Mafruz Ashrafi
      Pages 97-110
    3. Yun Xiong, Ming Chen, Yangyong Zhu
      Pages 111-126
    4. Maja Hadzic, Fedja Hadzic, Tharam S. Dillon
      Pages 127-141
    5. Jackei H. K. Wong, Tharam S. Dillon, Allan K. Y. Wong, Wilfred W. K. Lin
      Pages 143-157
    6. Ubaudi A. Franco, J. Kennedy Paul, R. Catchpoole Daniel, Guo Dachuan, J. Simoff Simeon
      Pages 159-168
    7. Flora S. Tsai, Kap Luk Chan
      Pages 169-182
    8. Yang Liu, Xiaohui Yu, Xiangji Huang, Aijun An
      Pages 183-195
    9. Zhongzhi Shi, Huifang Ma, Qing He
      Pages 197-208
    10. T. Werth, M. Wörlein, A. Dreweke, I. Fischer, M. Philippsen
      Pages 209-223
    11. Vania Bogorny, Monica Wachowicz
      Pages 225-239
    12. E. Roma Neto, D. S. Hamburger
      Pages 241-251
    13. Ira Assent, Ralph Krieger, Petra Welter, Jörg Herbers, Thomas Seidl
      Pages 267-282

About this book


Data Mining for Business Applications presents state-of-the-art data mining research and development related to methodologies, techniques, approaches and successful applications. The contributions of this book mark a paradigm shift from "data-centered pattern mining" to "domain-driven actionable knowledge discovery (AKD)" for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future data mining research and development in the dialogue between academia and business.

Part I centers on developing workable AKD methodologies, including:

    • domain-driven data mining
    • post-processing rules for actions
    • domain-driven customer analytics
    • the role of human intelligence in AKD
    • maximal pattern-based cluster
    • ontology mining

Part II focuses on novel KDD domains and the corresponding techniques, exploring the mining of emergent areas and domains such as:

    • social security data
    • community security data
    • gene sequences
    • mental health information
    • traditional Chinese medicine data
    • cancer related data
    • blog data
    • sentiment information
    • web data
    • procedures
    • moving object trajectories
    • land use mapping
    • higher education data
    • flight scheduling
    • algorithmic asset management

Researchers, practitioners and university students in the areas of data mining and knowledge discovery, knowledge engineering, human-computer interaction, artificial intelligence, intelligent information processing, decision support systems, knowledge management, and KDD project management are sure to find this a practical and effective means of enhancing their understanding of and using data mining in their own projects.


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

Editors and affiliations

  • Longbing Cao
    • 1
  • Philip S. Yu
    • 2
  • Chengqi Zhang
    • 3
  • Huaifeng Zhang
    • 1
  1. 1.School of Software Faculty of Engineering and Information TechnologyUniversity of TechnologySydneyAustralia
  2. 2.Department of Computer ScienceUniversity of Illinois at ChicagoChicago
  3. 3.Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information TechnologyUniversity of TechnologySydneyAustralia

Bibliographic information

Industry Sectors
IT & Software
Consumer Packaged Goods
Finance, Business & Banking
Energy, Utilities & Environment
Oil, Gas & Geosciences


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)