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.

Actionable knowledge

Actionable knowledge refers to the knowledge that can inform decision-making actions and be converted to decision-making actions.[5][7] 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 [8][9] and/or subjective [10] perspectives.

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.
  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.
  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.
  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. 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.
  8. Hilderman, R.; Hamilton, H. (2000). "Applying Objective Interestingness Measures in Data Mining Systems". Pkdd2000: 432–439.
  9. Freitas, A. (1998). "On Objective Measures of Rule Surprisingness". Proc. European Conf. Principles and Practice of Knowledge Discovery in Databases: 1–9.
  10. Liu, B. (2000). "Analyzing the Subjective Interestingness of Association Rules". IEEE Intelligent Systems. 15 (5): 47–55. doi:10.1109/5254.889106.
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