Domain Driven Data Mining (DDDM, D3M), or Domain-Driven Actionable Knowledge Discovery and Delivery (AKD), aims at next-generation data mining methodologies, techniques and real-life enterprise applications by involving and synthesizing ubiquitous data, information, resources and intelligence in data mining problems and environment as per requirements, and delivering actionable outcomes satisfying both technical significance and business needs and supporting direct decision-making actions for business.
DDDM Aims
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to design next-generation data mining methodology for actionable knowledge discovery and delivery, toward handling critical issues for KDD to effectively and efficiently contribute to real-world smart businesses and smart decision and benefit critical domain problems in theory and practice;
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to devise domain-driven data mining techniques to bridge the gap between a converted problem and its actual business problem, between academic objectives and business goals, between technical significance and business interest, and between identified patterns and business expected deliverables, toward strengthening business intelligence in complex enterprise applications;
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to present the applications of domain-driven data mining and demonstrate how KDD can be effectively deployed to solve complex practical problems; and
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to identify challenges and future directions for data mining research and development in the dialogue between academia and industry.
DDDM Concept Map
The following figure shows the concept map of DDDM.
Figure. The concept map of DDDM.
Also refer to Topics and Concepts for more information.
