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Issue No. 09 - September (2010 vol. 22)
ISSN: 1041-4347
pp: 1299-1312
Longbing Cao , University of Technology, Sydney, New South Wales
Huaifeng Zhang , University of Technology, Sydney, New South Wales
E.K. Park , College of Staten Island, City University of New York, Staten Island
Dan Luo , University of Technology, Sydney, New South Wales
Yanchang Zhao , University of Technology, Sydney, New South Wales
Chengqi Zhang , University of Technology, Sydney, New South Wales
ABSTRACT
Most data mining algorithms and tools stop at the mining and delivery of patterns satisfying expected technical interestingness. There are often many patterns mined but business people either are not interested in them or do not know what follow-up actions to take to support their business decisions. This issue has seriously affected the widespread employment of advanced data mining techniques in greatly promoting enterprise operational quality and productivity. In this paper, we present a formal view of actionable knowledge discovery (AKD) from the system and decision-making perspectives. AKD is a closed optimization problem-solving process from problem definition, framework/model design to actionable pattern discovery, and is designed to deliver operable business rules that can be seamlessly associated or integrated with business processes and systems. To support such processes, we correspondingly propose, formalize, and illustrate four types of generic AKD frameworks: Postanalysis-based AKD, Unified-Interestingness-based AKD, Combined-Mining-based AKD, and Multisource Combined-Mining-based AKD (MSCM-AKD). A real-life case study of MSCM-based AKD is demonstrated to extract debt prevention patterns from social security data. Substantial experiments show that the proposed frameworks are sufficiently general, flexible, and practical to tackle many complex problems and applications by extracting actionable deliverables for instant decision making.
INDEX TERMS
Data mining, domain-driven data mining (D^3M), actionable knowledge discovery, decision making.
CITATION
Longbing Cao, Huaifeng Zhang, E.K. Park, Dan Luo, Yanchang Zhao, Chengqi Zhang, "Flexible Frameworks for Actionable Knowledge Discovery", IEEE Transactions on Knowledge & Data Engineering, vol. 22, no. , pp. 1299-1312, September 2010, doi:10.1109/TKDE.2009.143
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