Actionable Knowledge Discovery using MSCAM
نویسنده
چکیده
Actionable Association Rule mining (AAR) is a closed optimization problem solving process from problem definition, model design to actionable pattern discovery, and is designed to deliver apt business rules that can be integrated with business processes and technical aspects. To support such processes, we correspondingly propose, formalize and illustrate a generic AAR model design: Multisource Combined-Mining-based AAR (MSCM-AAR). In this paper, we present a view of actionable association rule (AAR) from the technical and decision-making perspectives. A real-life case study of MSCM-based AAR is demonstrated to extract debt prevention patterns from social security data. Substantial experiments show that the proposed model design are sufficiently general, flexible and practical to tackle many complex problems and applications by extracting actionable deliverables for instant decision making.
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