Domain driven data mining

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.

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.^[7] 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.^[8]^[9]

^ Cao, L.; Zhao, Y.; Yu, P.; Zhang, C. (2010). Domain Driven Data Mining. Springer. ISBN 978-1-4419-5737-5.
^ Zhang, C.; Yu, P. S.; Bell, D. (June 2010). "IEEE TKDE Special Issue on Domain-driven Data Mining". IEEE Transactions on Knowledge and Data Engineering. 22 (6): 753–754. doi:10.1109/TKDE.2010.74. S2CID 29503757.
^ Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P. (1996). "From Data Mining to Knowledge Discovery in Databases". AI Magazine. 17 (3): 37–54.
^ Fayyad, U.; et al. (2003). "Summary from the KDD-03 Panel—Data Mining: The Next 10 Years". ACM SIGKDD Explorations Newsletter. 5 (2): 191–196. doi:10.1145/980972.981004. S2CID 37284526.
^ Cao, L.; Zhang, C.; Yang, Q.; Bell, D.; Vlachos, M.; Taneri, B.; Keogh, E.; Yu, P.; Zhong, N.; et al. (2007). "Domain-Driven, Actionable Knowledge Discovery". IEEE Intelligent Systems. 22 (4): 78–89. doi:10.1109/MIS.2007.67. S2CID 15928505.
^ Fayyad, U.; Smyth, P. (1996). "From Data Mining to Knowledge Discovery: An Overview". Advances in Knowledge Discovery and Data Mining, (U. Fayyad and P. Smyth, Eds.): 1–34.
^ "DDDM".
^ "International Workshop on Domain-driven Data Mining (DDDM)".
^ "International Journal of Data Science and Analytics".

[1] Cao, L.; Zhao, Y.; Yu, P.; Zhang, C. (2010). Domain Driven Data Mining. Springer. ISBN 978-1-4419-5737-5.

[2] Zhang, C.; Yu, P. S.; Bell, D. (June 2010). "IEEE TKDE Special Issue on Domain-driven Data Mining". IEEE Transactions on Knowledge and Data Engineering. 22 (6): 753–754. doi:10.1109/TKDE.2010.74. S2CID 29503757.

[:1-3] Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P. (1996). "From Data Mining to Knowledge Discovery in Databases". AI Magazine. 17 (3): 37–54.

[:2-4] Fayyad, U.; et al. (2003). "Summary from the KDD-03 Panel—Data Mining: The Next 10 Years". ACM SIGKDD Explorations Newsletter. 5 (2): 191–196. doi:10.1145/980972.981004. S2CID 37284526.

[:0-5] Cao, L.; Zhang, C.; Yang, Q.; Bell, D.; Vlachos, M.; Taneri, B.; Keogh, E.; Yu, P.; Zhong, N.; et al. (2007). "Domain-Driven, Actionable Knowledge Discovery". IEEE Intelligent Systems. 22 (4): 78–89. doi:10.1109/MIS.2007.67. S2CID 15928505.

[6] Fayyad, U.; Smyth, P. (1996). "From Data Mining to Knowledge Discovery: An Overview". Advances in Knowledge Discovery and Data Mining, (U. Fayyad and P. Smyth, Eds.): 1–34.

[7] "DDDM".

[8] "International Workshop on Domain-driven Data Mining (DDDM)".

[9] "International Journal of Data Science and Analytics".

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