Interestingness Measures for Actionable Patterns

  • Li-Shiang Tsay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8537)


The ability to make mined patterns actionable is becoming increasingly important in today’s competitive world. Standard data mining focuses on patterns that summarize data and these patterns are required to be further processed in order to determine opportunities for action. To address this problem, it is essential to extract patterns by comparing the profiles of two sets of relevant objects to obtain useful, understandable, and workable strategies. In this paper, we present the definition of actionable rules by integrating action rules and reclassification rules to build a framework for analyzing big data. In addition, three new interestingness measures, coverage, leverage, and lift, are proposed to address the limitations of minimum left support, right support and confidence thresholds for gauging the importance of discovered actionable rules.


Action Rule Reclassification Model Interestingness Measures actionability 


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  1. 1.
    Fem, H.: Four Vendor Views on Big Data and Big Data Analytics: IBM. IBM (2012)Google Scholar
  2. 2.
    Gantz, J., Reinsel, D.: The 2011 IDC Universe Digital study... Extracting Values from Chaos. IDC (2011)Google Scholar
  3. 3.
    Troester, M.: Big Data Meets Big Data analytics. SAS (2012)Google Scholar
  4. 4.
    Dumbill, E.: Volume, Velocity, Variety: What You Need to Know About Big Data. [Online] (2012)Google Scholar
  5. 5.
    Madden, S.: From Database to Big Data. IEEE Internet Computing 16(3), 4–6 (2012)CrossRefGoogle Scholar
  6. 6.
    Domingos, P.: Toward knowledge-rich data mining. Data Mining Knowledge Discovery 15(1), 21–28 (2007)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: The ACM SIGMOD International Conference on the Management of Data, pp. 207–216 (1993)Google Scholar
  8. 8.
    Grzymala-Busse, J.: A new version of the rule induction system LERS. Fundamenta Informaticae 31(1), 27–39 (1997)zbMATHGoogle Scholar
  9. 9.
    Quinlan, J.R.: C4.5: program for machine learning. Morgan Kaufmann (1992)Google Scholar
  10. 10.
    Raś, Z.W., Wieczorkowska, 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
  11. 11.
    Tsay, L.-S., Raś, Z.W., Im, S.: Reclassification Rules. In: ICDM Workshops Proceedings, Pisa, Italy, pp. 619–627. IEEE Computer Society (2008)Google Scholar
  12. 12.
    Raś, Z.W., Tsay, L.-S.: Discovering extended action-rules (System DEAR). In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds.) Proceedings of the Intelligent Information Systems Symposium. ASC, vol. 22, pp. 293–300. Springer, Heidelberg (2003)Google Scholar
  13. 13.
    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
  14. 14.
    Pawlak, Z.: Information systems - theoretical foundations. Information Systems Journal 6, 205–218 (1981)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Li-Shiang Tsay
    • 1
  1. 1.School of Tech.North Carolina A&T State Univ.GreensboroUSA

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