Domain driven data mining is a data mining methodology for discovering actionable knowledge and deliver actionable insights from complex data and behaviors in a complex environment. It studies the corresponding foundations, frameworks, algorithms, models, architectures, and evaluation systems for actionable knowledge discovery.
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Data-driven pattern mining and knowledge discovery in databases face such challenges that the discovered outputs are often not actionable. In the era of big data, how to effectively discover actionable insights from complex data and environment is critical. A significant paradigm shift is the evolution from data-driven pattern mining to domain-driven actionable knowledge discovery. Domain driven data mining is to enable the discovery and delivery of actionable knowledge and actionable insights.
Actionable knowledge
Actionable knowledge refers to the knowledge that can inform decision-making actions and be converted to decision-making actions. The actionability of data mining and machine learning findings, also called knowledge actionability, refers to the satisfaction of both technical (statistical) and business-oriented evaluation metrics or measures in terms of objective and/or subjective perspectives.
Actionable insight
Actionable insight enables accurate and in-depth understanding of things or objects and their characteristics, events, stories, occurrences, patterns, exceptions, and evolution and dynamics hidden in the data world and corresponding decision-making actions on top of the insights. Actionable knowledge may disclose actionable insights.