Domain driven data mining

Domain driven data mining is a <a href="/facts/Data_mining/AH53q5ac">data mining</a> methodology for discovering actionable knowledge and deliver actionable insights from complex data and <a href="/facts/Behavior/0Y1VqmVX">behaviors</a> in a complex environment. It studies the corresponding foundations, frameworks, algorithms, models, architectures, and evaluation systems for actionable knowledge discovery.
<a href="/facts/Data_mining/AH53q5ac">Data-driven pattern mining</a> and <a href="/facts/Knowledge_Discovery_in_Databases/AH53q5ac">knowledge discovery in databases</a> face such challenges that the discovered outputs are often not actionable. In the era of <a href="/facts/Big_data/XGtrVg4z">big data</a>, 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 <a href="/facts/Domain_knowledge/cbEFRF9v">domain knowledge</a> into data mining processes and models, such as deep neural networks, graph embedding, text mining, and reinforcement learning, is critically important.

Domain driven data mining open-in-new

Domain driven data mining