Research on Domain-Driven Actionable Knowledge Discovery

  • Zhengxiang Zhu
  • Jifa Gu
  • Lingling Zhang
  • Wuqi Song
  • Rui Gao
Part of the Communications in Computer and Information Science book series (CCIS, volume 35)


Traditional data mining is a data-driven trial-and-error process, stop on general pattern discovered. However, in many cases the mined knowledge by this process could not meet the real-world business needs. Actually, in real-world business, knowledge must be actionable, that is to say, one can do something on it to profit. Actionable knowledge discovery is a complex task, due to it is strongly depend on domain knowledge, such as background knowledge expert experience, user interesting, environment context, business logic, even including law, regulation, habit, culture etc. The main challenge is moving data-driven into domain-driven data mining (DDDM), its goal is to discover actionable knowledge rather than general pattern. As a new generation data mining approach, main ideas of the DDDM are introduced. Two types of process models show the difference between loosely coupled and tightly coupled. Also the main characteristics, such as constraint-base, human-machine cooperated, loop-closed iterative refinement and meta-synthesis-base process management are proposed. System architecture will be introduced, as well as a paradigm will be introduced.


Data Mining Domain Knowledge Actionable Knowledge Business Logic Actionable Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Frawley, W., Piatetsky-Shapiro, G., Matheus, C.: Knowledge Discovery in Databases: An Overview. AI Magazine 13, 213–228 (1992)Google Scholar
  2. 2.
    Terrance, G.: From Data To Actionable Knowledge:Applying Data Mining to the Problem of Intrusion Detection. In: The 2000 International Conference on Artificial Intelligence (2000)Google Scholar
  3. 3.
    He, Z., Xu, X., Deng, S.: Data mining for actionable knowledge: A survey, Technical report, Harbin Institute of Technology China (2005)Google Scholar
  4. 4.
    Cao, L.B., Zhang, C.Q.: Domain-driven actionable knowledge discovery in the real world. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 821–830. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Cao, L., et al.: Domain-driven in-depth pattern discovery: a practical methodology. In: Proceeding of AusDM, pp. 101–114 (2005)Google Scholar
  6. 6.
    Lin, T.Y., Yao, Y.Y., Louie, E.: Mining value added association rules. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS, vol. 2336, pp. 328–334. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Ras, Z.W., Wieczorkowska, A.: Action-rules: How to increase profit of a Company. In: Proc. Of ICDM 2002 (2002)Google Scholar
  8. 8.
    Yang, Q., Yin, J., Lin, C.X., Chen, T.: Postprocessing decision trees to extract actionable knowledge. In: Proc. of ICDM 2003 (2003)Google Scholar
  9. 9.
    Yang, Q., Yin, J., Ling, C., Pan, R.: Extractiong Actionable Knowledge from Decision Tress. IEEE Transactions On Knowledge And Data Engineering 19(1), 43–56 (2007)CrossRefGoogle Scholar
  10. 10.
    Han, J.W., Laks, V., Lakshmanan, S., Ng, R.T.: Constraint-Based, Multidimensional Data Mining. Computer 32(8), 46–50 (1999)CrossRefGoogle Scholar
  11. 11.
    Kovalerchuk, B.: Advanced data mining, link discovery and visual correlation for data and image analysis. In: International Conference on Intelligence Analysis (IA 2005), McLean, VA, May 2 (2005)Google Scholar
  12. 12.
    Tsay, L.-S., Raś, Z.W.: Discovering the concise set of actionable patterns. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds.) Foundations of Intelligent Systems. LNCS, vol. 4994, pp. 169–178. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
  14. 14.
    Zhu, Z.X., Gu, J.F.: Research on Domain Driven Depth Knowledge Discovery Based on Meta-synthesis. In: The theme for the 15th annual conference of systems engineering society of china, pp. 121–127 (2008)Google Scholar
  15. 15.
    Tang, X.J., Nie, K., Liu, Y.j.: Meta-systemsis approach to exploring constructing comprehensive transportation system in china. Journal of systems science and systems engineering 14(4), 476–494 (2005)CrossRefGoogle Scholar
  16. 16.
    Qian, X.S., Yuan, J.Y., Dai, R.W.: A new discipline of science- the study of open complex giant systems and its methodology. Journal of Systems Engineering & Electronic 4(2), 2–12 (1993)Google Scholar
  17. 17.
    Gu, J.F., Wang, H.C., Tang, X.J.: Meta-Synthesis Method System and Systematology Research. Science Press (2006) (in Chinese) Google Scholar
  18. 18.
    Kerdprasop, N., Kerdprasop, K.: Moving Data Mining Tools toward a Business Intelligence System. In: Proceedings of world academy of science, engineering and technology, vol. 21 (January 2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Zhengxiang Zhu
    • 1
  • Jifa Gu
    • 2
  • Lingling Zhang
    • 3
  • Wuqi Song
    • 1
  • Rui Gao
    • 4
  1. 1.Institute of Systems EngineeringDalian University of TechnologyDalianChina
  2. 2.Institute of Systems ScienceChinese Academy of SciencesBeijingChina
  3. 3.School of ManagementGraduate University of Chinese Academy of SciencesBeijingChina
  4. 4.Xiyuan Hospital of China Academy of Chinese Medical SciencesBeijingChina

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