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Issue No. 04 - July/August (2007 vol. 22)
ISSN: 1541-1672
pp: 78-88, c3
Michail Vlachos , IBM T.J. Watson Research Center
Qiang Yang , Hong Kong University of Science and Technology
Bahar Taneri , Scripps Genome Center
Ning Zhong , Maebashi Institute of Technology
Eugene Dubossarsky , Ernst & Young
David Bell , Queen's University Belfast
Mafruz Zaman Ashrafi , Institute for Infocomm Research
David Taniar , Monash University
Warwick Graco , Australian Taxation Office
Longbing Cao , University of Technology, Sydney
Philip S. Yu , IBM Thomas J. Watson Research Center
Eamonn Keogh , University of California, Riverside
Chengqi Zhang , University of Technology, Sydney
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.
INDEX TERMS
data mining, data models, database searching, knowledge engineering, visualization
CITATION
Michail Vlachos, Qiang Yang, Bahar Taneri, Ning Zhong, Eugene Dubossarsky, David Bell, Mafruz Zaman Ashrafi, David Taniar, Warwick Graco, Longbing Cao, Philip S. Yu, Eamonn Keogh, Chengqi Zhang, "Domain-Driven, Actionable Knowledge Discovery", IEEE Intelligent Systems, vol. 22, no. , pp. 78-88, c3, July/August 2007, doi:10.1109/MIS.2007.67
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