Domain Driven Data Mining (D3M)

In deploying data mining into the real-world business, we have to cater for business scenarios, organizational factors, user preferences and business needs. However, the current data mining algorithms and tools often stop at the delivery of patterns satisfying expected technical interestingness. Business people are not informed about how and what to do to take over the technical deliverables. The gap between academia and business has seriously affected the widespread employment of advanced data mining techniques in greatly promoting enterprise operational quality and productivity. To narrow down the gap, cater for realworld factors relevant to data mining, and make data mining workable in supporting decision-making actions in the real world, we propose the methodology of domain driven data mining (D
³
M for short). D
³
M aims to construct next-generation methodologies, techniques and tools for a possible paradigm shift from data-centered hidden pattern mining to domain-driven actionable knowledge delivery. In this talk, we address the concept map of D
³
M, theoretical underpinnings, several general and flexible frameworks, research issues, possible directions, application areas etc. related to D
³
M. Real-world case studies in financial data mining and social security mining are demonstrated to show the effectiveness and applicability of D
³
M in both research and development of real-world challenging problems.