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]

Actionable knowledge

Actionable knowledge refers to the knowledge that can inform decision-making actions and be converted to decision-making actions.^[5][10] 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 ^[11][12] and/or subjective ^[13] 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^[14][15] by mining complex data for complex knowledge in either a multi-feature, multi-source, or multi-method scenario.^[16]

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

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.
3. Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P. (1996). "From Data Mining to Knowledge Discovery in Databases". AI Magazine. 17 (3): 37–54.
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.
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".
10. Yang, Q.; et al. (2007). "Extracting Actionable Knowledge from Decision Trees". IEEE Trans. Knowledge and Data Engineering. 19 (1): 43–56. doi:10.1109/TKDE.2007.250584. S2CID 18053232.
11. Hilderman, R.; Hamilton, H. (2000). "Applying Objective Interestingness Measures in Data Mining Systems". Pkdd2000: 432–439.
12. Freitas, A. (1998). "On Objective Measures of Rule Surprisingness". Proc. European Conf. Principles and Practice of Knowledge Discovery in Databases: 1–9.
13. Liu, B. (2000). "Analyzing the Subjective Interestingness of Association Rules". IEEE Intelligent Systems. 15 (5): 47–55. doi:10.1109/5254.889106.
14. 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
15. Longbing Cao. Actionable Knowledge Discovery and Delivery, WIREs Data Mining and Knowledge Discovery, 2(2): 149-163, 2012
16. 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