Jump to content

Domain driven data mining

Add links
From Wikipedia, the free encyclopedia
This is an old revision of this page, as edited by BG19bot (talk | contribs) at 05:24, 25 April 2016 (WP:CHECKWIKI error fix for #16. Remove invisible Unicode characters. Do general fixes if a problem exists. -, replaced: → using AWB). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Domain driven data mining,[1] also called actionable knowledge discovery, is a methodology proposed to discover actionable knowledge [2][3] and deliver actionable insights from complex data and behaviors in a complex environment. Domain driven data mining studies the corresponding foundations, frameworks, algorithms, models, architectures, and evaluation systems for actionable knowledge discovery.

Classic data-driven pattern mining to knowledge discovery [4] face challenges [5] 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 from data-driven knowledge discovery to domain-driven actionable knowledge discovery.[3][6] 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.

Actionable knowledge

Actionable knowledge[7][8] refers to the knowledge that can inform decision-making actions and be converted to decision-making actions. In general, only objective technical interestingness[9] is evaluated in data mining and knowledge discovery. This has been shown insufficient.[10] The actionability of data mining and machine learning findings, or knowledge actionability refers to the satisfaction of both technical (statistical) and business-oriented evaluation metrics or measures in terms of objective and/or subjective 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.

Further reading

  • 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.
  • Philip Yu, Chengqi Zhang, et al. 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. ^ 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)
  3. ^ a b 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)
  4. ^ Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P. (1996). "From Data Mining to Knowledge Discovery in Databases". AI Magazine. 17 (3): 37–54.
  5. ^ Cao, L. (2010). "Domain driven data mining: challenges and prospects". IEEE Trans. on Knowledge and Data Engineering. 22 (6): 755–769.
  6. ^ 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)
  7. ^ Cao, L. (2012). "Actionable Knowledge Discovery and Delivery". WIREs Data Mining and Knowledge Discovery. 2 (2): 149–163.
  8. ^ 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)
  9. ^ Hilderman, R.; Hamilton, H. (2000). "Applying Objective Interestingness Measures in Data Mining Systems". PKDD2000: 432–439.
  10. ^ Liu, B. (2000). "Analyzing the Subjective Interestingness of Association Rules". IEEE Intelligent Systems. 15 (5): 47–55.
Retrieved from "https://en.wikipedia.org/w/index.php?title=Domain_driven_data_mining&oldid=717009777"