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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 [[behavior]]s in a complex environment. It studies the corresponding foundations, frameworks, algorithms, models, architectures, and evaluation systems for actionable knowledge discovery.<ref>((Cite book|title=Domain Driven Data Mining|last1=Cao|first1=L.|last2=Zhao|first2=Y.|last3=Yu|first3=P.|last4=Zhang|first4=C.|publisher=Springer|year=2010|isbn=978-1-4419-5737-5))</ref><ref>((Cite journal|title=IEEE TKDE Special Issue on Domain-driven Data Mining|journal=IEEE Transactions on Knowledge and Data Engineering|volume=22|issue=6|pages=753–754|date=June 2010|doi=10.1109/TKDE.2010.74|last1=Zhang|first1=C.|last2=Yu|first2=P. S.|last3=Bell|first3=D.|s2cid=29503757 ))</ref>

[[Data mining|Data-driven pattern mining]] and [[Knowledge Discovery in Databases|knowledge discovery in databases]]<ref name=":1">((Cite journal|last1=Fayyad|first1=U.|last2=Piatetsky-Shapiro|first2=G.|last3=Smyth|first3=P.|year=1996|title=From Data Mining to Knowledge Discovery in Databases|journal=AI Magazine|volume=17|issue=3|pages=37–54))</ref> 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.<ref name=":2">((Cite journal|last=Fayyad|first=U.|year=2003|title=Summary from the KDD-03 Panel—Data Mining: The Next 10 Years|journal=ACM SIGKDD Explorations Newsletter|volume=5|issue=2|pages=191–196|doi=10.1145/980972.981004|s2cid=37284526|display-authors=etal))</ref><ref name=":0">((Cite journal|last1=Cao|first1=L.|last2=Zhang|first2=C.|last3=Yang|first3=Q.|last4=Bell|first4=D.|last5=Vlachos|first5=M.|last6=Taneri|first6=B.|last7=Keogh|first7=E.|last8=Yu|first8=P.|last9=Zhong|first9=N.|year=2007|title=Domain-Driven, Actionable Knowledge Discovery|journal=IEEE Intelligent Systems|volume=22|issue=4|pages=78–89|doi=10.1109/MIS.2007.67|s2cid=15928505|display-authors=etal))</ref><ref>((Cite journal|last1=Fayyad|first1=U.|last2=Smyth|first2=P.|year=1996|title=From Data Mining to Knowledge Discovery: An Overview|journal=Advances in Knowledge Discovery and Data Mining, (U. Fayyad and P. Smyth, Eds.)|pages=1–34))</ref> 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.<ref>((Cite web|url=https://www.datasciences.org/dddm/|title = DDDM))</ref>
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.<ref>((Cite web|url=https://datamining.utk.edu/content/dddm/|title = International Workshop on Domain-driven Data Mining (DDDM)))</ref><ref>((Cite web|url=https://www.springer.com/journal/41060/updates/18782360|title = International Journal of Data Science and Analytics))</ref>

== Actionable knowledge ==

Actionable knowledge refers to the [[knowledge]] that can inform [[decision-making]] actions and be converted to decision-making actions.<ref name=":0"/><ref>((Cite journal|last=Yang|first=Q.|year=2007|title=Extracting Actionable Knowledge from Decision Trees|journal=IEEE Trans. Knowledge and Data Engineering|volume=19|issue=1|pages=43–56|doi=10.1109/TKDE.2007.250584|s2cid=18053232|display-authors=etal))</ref>  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 <ref>((Cite journal|last1=Hilderman|first1=R.|last2=Hamilton|first2=H.|year=2000|title=Applying Objective Interestingness Measures in Data Mining Systems|journal=Pkdd2000|pages=432–439))</ref><ref>((Cite journal|last=Freitas|first=A.|year=1998|title=On Objective Measures of Rule Surprisingness|journal=Proc. European Conf. Principles and Practice of Knowledge Discovery in Databases|pages=1–9))</ref> and/or subjective <ref>((Cite journal|last=Liu|first=B.|year=2000|title=Analyzing the Subjective Interestingness of Association Rules|journal=IEEE Intelligent Systems|volume=15|issue=5|pages=47–55|doi=10.1109/5254.889106))</ref> 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''<ref>Longbing Cao, Yanchang Zhao, Huaifeng Zhang, Dan Luo, Chengqi Zhang. Flexible Frameworks for Actionable Knowledge Discovery, IEEE Trans. on Knowledge and Data Engineering, 22(9): 1299-1312, 2010</ref><ref>Longbing Cao. Actionable Knowledge Discovery and Delivery, WIREs Data Mining and Knowledge Discovery, 2(2): 149-163, 2012</ref> by mining complex data for complex knowledge in either a multi-feature, multi-source, or multi-method scenario.<ref>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</ref>

== 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))

[[Category:Data mining]]

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