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Domain-Driven, Actionable Knowledge Discovery
July/August 2007 (vol. 22 no. 4)
pp. 78-88, c3
Longbing Cao, University of Technology, Sydney
Chengqi Zhang, University of Technology, Sydney
Qiang Yang, Hong Kong University of Science and Technology
David Bell, Queen's University Belfast
Michail Vlachos, IBM T.J. Watson Research Center
Bahar Taneri, Scripps Genome Center
Eamonn Keogh, University of California, Riverside
Philip S. Yu, IBM Thomas J. Watson Research Center
Ning Zhong, Maebashi Institute of Technology
Mafruz Zaman Ashrafi, Institute for Infocomm Research
David Taniar, Monash University
Eugene Dubossarsky, Ernst & Young
Warwick Graco, Australian Taxation Office
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.
Index Terms:
data mining, data models, database searching, knowledge engineering, visualization
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, "Domain-Driven, Actionable Knowledge Discovery," IEEE Intelligent Systems, vol. 22, no. 4, pp. 78-88, c3, July-Aug. 2007, doi:10.1109/MIS.2007.67
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