DocumentCode :
1071385
Title :
Flexible Frameworks for Actionable Knowledge Discovery
Author :
Cao, Longbing ; Zhao, Yanchang ; Zhang, Huaifeng ; Luo, Dan ; Zhang, Chengqi ; Park, E.K.
Author_Institution :
Fac. of Eng. & Inf. Technol., Univ. of Technol., Sydney, NSW, Australia
Volume :
22
Issue :
9
fYear :
2010
Firstpage :
1299
Lastpage :
1312
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.
Keywords :
data mining; decision making; optimisation; business processes; combined-mining-based actionable knowledge discovery; decision-making perspectives; enterprise operational quality; flexible frameworks; multisource combined-mining-based actionable knowledge discovery; optimization problem-solving process; post analysis-based actionable knowledge discovery; social security data; unified-interestingness-based actionable knowledge discovery; Data mining; actionable knowledge discovery; decision making.; domain-driven data mining (D^3M);
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2009.143
Filename :
5072220
Link To Document :
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