Domain-Driven, Actionable Knowledge Discovery

  • Authors:
  • Longbing Cao;Chengqi Zhang;Qiang Yang;David Bell;Michail Vlachos;Bahar Taneri;Eamonn Keogh;Philip S. Yu;Ning Zhong;Mafruz Zaman Ashrafi;David Taniar;Eugene Dubossarsky;Warwick Graco

  • Affiliations:
  • University of Technology, Sydney;University of Technology, Sydney;Hong Kong University of Science and Technology;Queen's University Belfast;IBM T.J. Watson Research Center;Scripps Genome Center;University of California, Riverside;IBM Thomas J. Watson Research Center;Maebashi Institute of Technology;Institute for Infocomm Research;Monash University;Ernst & Young;Australian Taxation Office

  • Venue:
  • IEEE Intelligent Systems
  • Year:
  • 2007

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Abstract

Existing knowledge discovery and data mining (KDD) field seldom deliver results that businesses can act on directly. This issue, Trends & Controversies presents seven short articles reporting on different aspects of domain-driven KDD, an R&D area that targets the development of effective methodologies and techniques for delivering actionable knowledge in a given domain, especially business.