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Domain driven data mining

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Domain driven data mining [1][2], also called actionable knowledge discovery, is a methodology proposed to discover actionable knowledge [3][4] and deliver actionable insights from complex data and behaviors in a complex environment . Domain driven data mining studies the corresponding concepts, foundations, frameworks [5], algorithms, models, architectures, infrastructures, evaluation systems, dissemination, communication, knowledge management, and project management for enabling the above objective.

Typical domain driven data mining tasks and techniques consist of combined mining [6][7], involving domain knowledge into data mining, post analysis and post mining [8] based actionable knowledge discovery, unified interestingness-based actionable knowledge discovery, combined interestingness-based actionable knowledge discovery, and human-machine-cooperated data mining.

Paradigm shift to actionable knowledge discovery

Classic data-driven pattern mining to knowledge discovery [9] face challenges [10] such as delivering findings that are often not actionable. In the era of big data, how to effectively discover actionable insights from data with sophisticated data characteristics and complexities is critical and very challenging. A significant paradigm shift is the evolution [11][12] from data-driven knowledge discovery to domain-driven actionable knowledge discovery [3][13]. To enable the discovery and delivery of actionable knowledge and actionable insights, in addition to data intelligence, other intelligences need to be involved in the data mining process and systems. Domain driven data mining aims to address such issues.

Intelligence meta-synthesis

A complex data mining process and system often involves ubiquitous intelligences [14], such as domain intelligence, behavior intelligence, network intelligence, social intelligence, human intelligence, and organizational intelligence. Domain driven data mining aims to synthesize these comprehensive intelligences for discovering and delivering actionable knowledge and insights through the methodology of qualitative-to-quantitative intelligence meta-synthesis [15].

Actionable knowledge

Actionable knowledge [16] refers to the knowledge that can inform decision-making actions and be converted to decision-making actions. Domain driven data mining enables actionable knowledge discovery [13], i.e., the discovery and delivery of actionable knowledge [17].

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.

Combined mining

Combined mining [6][7] is an approach to discovering actionable knowledge in complex data and environment, such as with multiple feature sets, in multiple data sources, by multiple methods, and for combined patterns and knowledge.

Knowledge actionability and evaluation systems

In general, only objective technical interestingness [18] is evaluated in data mining and knowledge discovery. This has been shown insufficient [19][20]. The actionability of data mining and machine learning findings, or knowledge actionability [20], refers to the satisfaction of both technical (statistical) and business-oriented evaluation metrics or measures in terms of objective and/or subjective perspectives. Accordingly, actionability may be evaluated in terms of subjective technical interestingness, objective technical interestingness, subjective business interestingness, and objective business interestingness, by following the classic interestingness [9] concept.

Further reading

  • Domain driven data mining
  • Longbing Cao, Chengqi Zhang. The evolution of KDD: Towards domain-driven data mining. International Journal of Pattern Recognition and Artificial Intelligence, 21(4): 677-692, 2007.
  • Longbing Cao. Introduction to Domain Driven Data Mining, in Data Mining for Business Applications (eds. Cao L, et al.), 3-10, 2008.
  • Philip Yu, Chengqi Zhang, Graham William, Longbing Cao, Yanchang Zhao. Proc. Of ACM SIGKDD Workshop on Domain Driven Data Mining (edited), ACM Press (978-1-59593-846-6), 2007.
  • Q. Yang, J. Yin, C. Ling, and R. Pan, “Extracting Actionable Knowledge from Decision Trees,” IEEE Trans. Knowledge and Data Eng., vol. 19, no. 1, pp. 43-56, Jan. 2007.
  • U. Fayyad and P. Smyth, “From Data Mining to Knowledge Discovery: An Overview,” Advances in Knowledge Discovery and Data Mining, U. Fayyad and P. Smyth, eds., pp. 1-34, 1996.
  • A. Freitas, “On Objective Measures of Rule Surprisingness,” Proc. European Conf. Principles and Practice of Knowledge Discovery in Databases (PKDD ’98), pp. 1-9, 1998

References

  1. ^ Cao, L.; Zhao, Y.; Yu, P.; Zhang, C. (2010). Domain Driven Data Mining. Springer. ISBN 978-1-4419-5737-5.
  2. ^ Cao, L.; Schurmann, R.; Zhang, C. (2005). "Domain-Driven In-Depth pattern Discovery: A Practical Methodology". AusDM2005: 101–114.
  3. ^ a b Cao, L.; et al. (2007). "Domain-Driven, Actionable Knowledge Discovery". IEEE Intelligent Systems. 22 (4): 78–89. {{cite journal}}: Explicit use of et al. in: |first= (help)
  4. ^ Zhao, Y.; et al. (2008). "Combined Pattern Mining: from Learned Rules to Actionable Knowledge". LNCS 5360: 393–403. {{cite journal}}: Explicit use of et al. in: |first= (help)
  5. ^ Cao, L.; et al. (2010). "Flexible Frameworks for Actionable Knowledge Discovery". IEEE Trans. on Knowledge and Data Engineering. 22 (9): 1299–1312. {{cite journal}}: Explicit use of et al. in: |first= (help)
  6. ^ a b Cao, L. (2013). "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.
  7. ^ a b Cao, L.; et al. (2011). "Combined Mining: Discovering Informative Knowledge in Complex Data". IEEE Trans. SMC Part B. 41 (3): 699–712. {{cite journal}}: Explicit use of et al. in: |first= (help)
  8. ^ Zhao, Y.; et al. (2009). Post-Mining of Association Rules. IGI Global. {{cite book}}: Explicit use of et al. in: |first= (help)
  9. ^ a b Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P. (1996). "From Data Mining to Knowledge Discovery in Databases". AI Magazine. 17 (3): 37–54.
  10. ^ Cao, L. (2010). "Domain driven data mining: challenges and prospects". IEEE Trans. on Knowledge and Data Engineering. 22 (6): 755–769.
  11. ^ Cao, L.; Zhang, C. (2007). "The evolution of KDD: Towards domain-driven data mining". International Journal of Pattern Recognition and Artificial Intelligence. 21 (4): 677–692.
  12. ^ 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)
  13. ^ a b Cao, L.; Zhang, C. (2006). "Domain-driven actionable knowledge discovery in the real world". LNAI 3918, PAKDD2006: 821–830.
  14. ^ Cao, L.; Luo, D.; Zhang, C. (2009). "Ubiquitous Intelligence in Agent Mining". LNCS5680, ADMI 2009: 23–35.
  15. ^ Qian, X.; Yu, J.; Dai, R. (1990). "A New Scientific Field: Open Complex Giant Systems and the Methodology". Chinese J. Nature. 13 (1): 3–10.
  16. ^ Cao, L. (2012). "Actionable Knowledge Discovery and Delivery". WIREs Data Mining and Knowledge Discovery. 2 (2): 149–163.
  17. ^ 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)
  18. ^ Hilderman, R.; Hamilton, H. (2000). "Applying Objective Interestingness Measures in Data Mining Systems". PKDD2000: 432–439.
  19. ^ Liu, B. (2000). "Analyzing the Subjective Interestingness of Association Rules". IEEE Intelligent Systems. 15 (5): 47–55.
  20. ^ a b Cao, L.; Luo, D.; Zhang, C. (2007). "Knowledge Actionability: Satisfying Technical and Business Interestingness". International Journal of Business Intelligence and Data Mining. 2 (4): 496–514.
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