Domain driven data mining
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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.[1][2]
Data-driven pattern mining and knowledge discovery in databases [3] 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.[4][5][6] 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.[5][7] 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 [8][9] and/or subjective [10] 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.
References
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