Advertisement

On Objective Measures of Actionability in Knowledge Discovery

Part of the Intelligent Systems Reference Library book series (ISRL, volume 43)

Abstract

One of the main goals of knowledge discovery is to find nuggets of useful knowledge that could influence or help users in a decision-making process. This task can be viewed as searching in an immense space for possible actionable concepts. Most of the KDD researchers believe that the task of finding actionable patterns is not easy and actionability is a purely subjective concept. Practitioners report that applying the KDD algorithms comprises not more than 20% of the knowledge discovery process and the remaining 80% relies on human experts to post-analyze the discovered patterns manually. To improve the effectiveness of the process, actionability can be defined as an objective measure via providing a well-defined strategy of pattern generations that allow guidance from domain experts at key stages in the search for useful patterns. The approach tightly integrates KDD and decision making by solving the decision-making problems directly on the core of KDD algorithms. In this paper, we present a granular computing-based method for generating a set of rules by utilizing the domain experts’ prior knowledge to formulate its inputs and to evaluate the observed regularities it discovers. The generated rule overcomes the traditional data-centered pattern mining, resulting to bridge the gap and enhance real-world problem-solving capabilities.

Keywords

Actionable patterns granular computing reclassification model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abramowicz, W., Zurada, J.M. (eds.): Knowledge Discovery for Business Information Systems. Kluwer, Dordrecht (2001)MATHGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithm for mining association rules. In: Proceeding of the Twentieth International Conference on VLDB, pp. 487–499 (1994)Google Scholar
  3. 3.
    Bobrowski, L.: HEPAR: Computer system for diagnosis support and data analysis. In: Prace IBIB 31, Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland (1992)Google Scholar
  4. 4.
    Cao, L.: Domain-driven data mining: Challenges and prospects. IEEE Transactions on Knowledge and Data Engineering 22(6), 755–769 (2010)CrossRefGoogle Scholar
  5. 5.
    Chmielewski, M.R., Grzymała-Busse, J., Peterson, N.W., Than, S.: The rule induction system LERS - a version for personal computers. Foundations of Computing and Decision Sciences 18(3-4), 181–121 (1993)Google Scholar
  6. 6.
    Domingos, P.: Toward knowledge-rich data mining. Data Mining Knowledge Discovery 15(1), 21–28 (2007)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Fayyad, U., Shapiro, G., Uthurusamy, R.: Summary from the KDD-03 panel Data Mining: The next 10 years. ACM SIGK Explorations Newsletter 5(2), 191–196 (2003)CrossRefGoogle Scholar
  8. 8.
    Greco, S., Matarazzo, B., Pappalardo, N., Słowiński, R.: Measuring expected effects of interventions based on decision rules. Journal of Experimental and Theoretical Artificial Intelligence 17(1-2), 103–118 (2005)MATHCrossRefGoogle Scholar
  9. 9.
    He, Z., Xu, X., Deng, S., Ma, R.: Mining action rules from scratch. Expert Systems with Applications 29(3), 691–699 (2005)CrossRefGoogle Scholar
  10. 10.
    Kriegel, H., Borgwardt, K., Kroger, P., Pryakhim, A., Schubert, M., Zimek, A.: Future trends in data mining. Data Mining and Knowledge Discovery 15(1), 87–97 (2007)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Liu, B., Hsu, W.: Post-analysis of learned rules. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI 1996), pp. 828–834. AAAI Press, Menlo Park (1996)Google Scholar
  12. 12.
    Major, J.A., Mangano, J.: Selecting among rules induced from a hurricane database. In: AAAI 1993 Workshop on Knowledge Discovery in Databases, pp. 24–44. AAAI Press, Menlo Park (1993)Google Scholar
  13. 13.
    Mitchell, T.: Machine learning and data mining. CACM 42(11), 31–36 (1999)Google Scholar
  14. 14.
    Onisko, A., Druzdzel, M., Wasyluk, H.: Extension of the HEPAR II model to multiple-disorder diagnosis. In: Intelligent Information Systems. ASC, pp. 303–313. Springer, Heidelberg (2000)Google Scholar
  15. 15.
    Pawlak, Z.: Information systems – theoretical foundations. Information Systems 6, 205–218 (1981)MATHCrossRefGoogle Scholar
  16. 16.
    Piatetsky-Shapiro, G., Matheus, C.J.: The interestingness of deviations. In: Proceedings of the AAAI 1994 Workshop on Knowledge Discovery in Databases, pp. 1–12 (1994)Google Scholar
  17. 17.
    Piatetsky-Shapiro, G.: Data Mining and Knowledge Discovery 1996 to 2005: Overcoming the hype and moving from “university” to “business” and “analystics”. Data Mining and Knowledge Discovery 15(1), 99–105 (2007)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Raś, Z.W., Tsay, L.-S.: Discovering extended action-rules (System DEAR). In: Intelligent Information Systems, Proceedings of the IIS 2003 Symposium. ASC, pp. 293–300. Springer, Heidelberg (2003)Google Scholar
  19. 19.
    Raś, Z.W., Wieczorkowska, A.A.: Action-Rules: How to Increase Profit of a Company. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 587–592. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  20. 20.
    Shapiro, G.P., Djeraba, C., Getoor, L., Grossman, R., Feldman, R., Zaki, M.: What are the grant challenges for data mining? In: KDD-2006 Panel Report, ACM SIGKDD Explorations Newletter (2006)Google Scholar
  21. 21.
    Silberschatz, A., Tuzhilin, A.: On subjective measures of interestingness in knowledge discovery. In: Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD 1995), Montreal, Canada, August 20-21, pp. 275–281. AAAI Press, Menlo Park (1995)Google Scholar
  22. 22.
    Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge and Data Engineering 8(6), 970–974 (1996)CrossRefGoogle Scholar
  23. 23.
    Tsay, L.-S., Raś, Z.W.: Action rules discovery: system DEAR2, method and experiments. Journal of Experimental and Theoretical Artificial Intelligence 17(1-2), 119–128 (2005)MATHCrossRefGoogle Scholar
  24. 24.
    Tsay, L.-S., Raś, Z.W.: Action Rules Discovery System DEAR_3. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds.) ISMIS 2006. LNCS (LNAI), vol. 4203, pp. 483–492. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  25. 25.
    Tsay, L.-S., Raś, Z.W.: E-action rules. In: Lin, T.Y., et al. (eds.) Data Mining: Foundations and Practice. SCI, vol. 118, pp. 277–288. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  26. 26.
    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 (LNAI), vol. 4994, pp. 169–178. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  27. 27.
    Tsay, L.-S., Raś, Z., Wieczorkowska, A.: Tree-based algorithm for discovering extended action-rules (System DEAR2). In: Intelligent Information Processing and Web Mining, Proceedings of the IIS 2004 Symposium. ASC, pp. 459–464. Springer, Heidelberg (2004)Google Scholar
  28. 28.
    Tsay, L.S., Raś, Z.W., Im, S.: Reclassification Rules. In: Proceedings of 2008 IEEE International Conference on Data Mining Workshops, Pisa, Italy, pp. 619–627. IEEE Computer Society (2008)Google Scholar
  29. 29.
    Webb, G.I.: Editorial. Data Mining and Knowledge Discovery 15(1), 1–2 (2007)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.North Carolina A&T State UniversityGreensboroUSA
  2. 2.Indiana UniversityIndianapolisUSA

Personalised recommendations