The Evolution of KDD: towards Domain-Driven Data Mining

Traditionally, data mining is an autonomous data-driven trial-and-error process. Its typical task is to let data tell a story disclosing hidden information regarding a business issue. Driven by this methodology, domain intelligence is not necessary in targeting the demonstration of an algorithm. As a result, very often knowledge discovered is not generally interesting to business needs. However, real-world applications expect knowledge for taking effective actions. To this end, this paper proposes domaindriven data mining methodology, which involves domain intelligence into mining actionable knowledge in constrained environment for satisfying user needs. Key components of domain-driven data mining are constrained context, integrating domain intelligence, human-machine cooperation, in-depth mining, actionability enhancement, and iterative refinement process. We illustrate two case studies of utilizing domain-driven data mining methodology: mining impact-targeted activity patterns and identifying stock trading patterns of interest to trading. The results show that domain-driven data mining has a potential for further enhancing the actionability of mined patterns in real-world situation.