Domain Driven Data Mining

  • Longbing Cao
  • Philip S. Yu
  • Chengqi Zhang
  • Yanchang Zhao

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Longbing Cao, Chengqi Zhang, Philip S. Yu, Yanchang Zhao
    Pages 1-25
  3. Longbing Cao, Chengqi Zhang, Philip S. Yu, Yanchang Zhao
    Pages 27-47
  4. Longbing Cao, Chengqi Zhang, Philip S. Yu, Yanchang Zhao
    Pages 49-73
  5. Longbing Cao, Chengqi Zhang, Philip S. Yu, Yanchang Zhao
    Pages 75-91
  6. Longbing Cao, Chengqi Zhang, Philip S. Yu, Yanchang Zhao
    Pages 93-112
  7. Longbing Cao, Chengqi Zhang, Philip S. Yu, Yanchang Zhao
    Pages 113-143
  8. Longbing Cao, Chengqi Zhang, Philip S. Yu, Yanchang Zhao
    Pages 145-169
  9. Longbing Cao, Chengqi Zhang, Philip S. Yu, Yanchang Zhao
    Pages 171-180
  10. Longbing Cao, Chengqi Zhang, Philip S. Yu, Yanchang Zhao
    Pages 181-201
  11. Longbing Cao, Chengqi Zhang, Philip S. Yu, Yanchang Zhao
    Pages 203-215
  12. Longbing Cao, Chengqi Zhang, Philip S. Yu, Yanchang Zhao
    Pages 217-219
  13. Longbing Cao, Chengqi Zhang, Philip S. Yu, Yanchang Zhao
    Pages 221-223
  14. Back Matter
    Pages 1-23

About this book

Introduction

In the present thriving global economy a need has evolved for complex data analysis to enhance an organization’s production systems, decision-making tactics, and performance. In turn, data mining has emerged as one of the most active areas in information technologies. Domain Driven Data Mining offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery.

About this book:

  • Enhances the actionability and wider deployment of existing data-centered data mining through a combination of domain and business oriented factors, constraints and intelligence.

  • Examines real-world challenges to and complexities of the current KDD methodologies and techniques.
  • Details a paradigm shift from "data-centered pattern mining" to "domain driven actionable knowledge discovery" for next-generation KDD research and applications.
  • Bridges the gap between business expectations and research output through detailed exploration of the findings, thoughts and lessons learned in conducting several large-scale, real-world data mining business applications
  • Includes techniques, methodologies and case studies in real-life enterprise data mining
  • Addresses new areas such as blog mining

Domain Driven Data Mining is suitable for 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.

Keywords

data analysis data mining decision support system information processing knowledge discovery knowledge management organization

Authors and affiliations

  • Longbing Cao
    • 1
  • Philip S. Yu
    • 2
  • Chengqi Zhang
    • 3
  • Yanchang Zhao
    • 4
  1. 1.Fac. Engineering & Information Tech.University of Technology, SydneyBroadwayAustralia
  2. 2.Dept. Computer ScienceUniversity of Illinois, ChicagoChicagoU.S.A.
  3. 3.Fac. Engineering & Information Tech.University of Technology, SydneyBroadwayAustralia
  4. 4.Fac. Engineering & Information Tech.University of Technology, SydneyBroadwayAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-5737-5
  • Copyright Information Springer Science+Business Media, LLC 2010
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science
  • Print ISBN 978-1-4419-5736-8
  • Online ISBN 978-1-4419-5737-5
  • About this book