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Issue No. 04 - July/August (2007 vol. 22)
ISSN: 1541-1672
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.
data mining, data models, database searching, knowledge engineering, visualization

M. Vlachos et al., "Domain-Driven, Actionable Knowledge Discovery," in IEEE Intelligent Systems, vol. 22, no. , pp. 78-88, c3, 2007.
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