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. ↑ "IEEE TKDE Special Issue on Domain-driven Data Mining".
  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.
  5. 1 2 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.
  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.
  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.
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