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Domain driven data mining

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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 behaviors in a complex environment. It studies the corresponding foundations, frameworks, algorithms, models, architectures, and evaluation systems for actionable knowledge discovery. [1][2]

Data 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.

To conduct domain driven data mining, domain knowledge usually needs to be involved. Other aspects including human, social and environmental factors may also need to considered.

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 [1], 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. ^ a b 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. {{cite journal}}: Explicit use of et al. in: |first= (help)
  5. ^ a b 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. {{cite journal}}: Explicit use of et al. in: |first9= (help)
  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. {{cite journal}}: Explicit use of et al. in: |first= (help)
  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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