Domain driven data mining is a data mining methodology for discovering actionable knowledge and deliver actionable insights from complex data and

behavior Behavior (American English) or behaviour ( British English) is the range of actions and mannerisms made by individuals, organisms, systems or artificial entities in some environment. These systems can include other systems or organisms as w ...

s in a complex environment. It studies the corresponding foundations, frameworks, algorithms, models, architectures, and evaluation systems for actionable knowledge discovery. 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. Domain driven data mining has attracted significant attention from both academic and industry. There was a workshop series on domain driven data mining during 2007-2014 with the IEEE International Conference on Data Mining and a special issue published by the IEEE Transactions on Knowledge and Data Engineering. There are also various new research problems and challenges in the last decade, where the incorporation of domain knowledge into data mining processes and models, such as deep neural networks, graph embedding, text mining, and reinforcement learning, is critically important.

Actionable knowledge

Actionable knowledge refers to the

knowledge Knowledge can be defined as awareness of facts or as practical skills, and may also refer to familiarity with objects or situations. Knowledge of facts, also called propositional knowledge, is often defined as true belief that is disti ...

that can inform

decision-making In psychology, decision-making (also spelled decision making and decisionmaking) is regarded as the cognitive process resulting in the selection of a belief or a course of action among several possible alternative options. It could be either r ...

actions and be converted to decision-making actions. The actionability of data mining and

machine learning Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine ...

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. The research and innovation on actionable knowledge discovery can be deemed a paradigm shift from ''knowledge discovery from data'' to ''actionable knowledge discovery and delivery'' by mining complex data for complex knowledge in either a multi-feature, multi-source, or multi-method scenario.Longbing Cao. Combined Mining: Analyzing Object and Pattern Relations for Discovering and Constructing Complex but Actionable Patterns, WIREs Data Mining and Knowledge Discovery, 3(2): 140-155, 2013

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

{{reflist Data mining