Evaluating Interestingness Measures with Linear Correlation Graph

  • Xuan-Hiep Huynh
  • Fabrice Guillet
  • Henri Briand
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


Making comparisons from the post-processing of association rules have become a research challenge in data mining. By evaluating interestingness value calculated from interestingness measures on association rules, a new approach based on the Pearson’s correlation coefficient is proposed to answer the question: How we can capture the stable behaviors of interestingness measures on different datasets?. In this paper, a correlation graph is used to evaluate the behavior of 36 interestingness measures on two datasets.


Association Rule Mining Association Rule Good Rule Interestingness Measure Correlation Graph 
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.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of 1993 ACM-SIGMOD International Conference on Management of Data, pp. 207–216. ACM Press, Washington (1993)CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB 1994, Proceedings of 20th International Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann Publishers Inc., Santiago (1994)Google Scholar
  3. 3.
    Bayardo Jr., R.J., Agrawal, R.: Mining the most interestingness rules. In: KDD 1999, Proceedings of the 5th ACM SIGKDD International Confeference On Knowledge Discovery and Data Mining, pp. 145–154. ACM Press, San Diego (1999)Google Scholar
  4. 4.
    Blanchard, J., Guillet, F., Gras, R., Briand, H.: Assessing rule interestingness with a probabilistic measure of deviation from equilibrium. In: ASMDA 2005, Proceedings of the 11th International Symposium on Applied Stochastic Models and Data Analysis, Brest, France, pp. 191–200 (2005)Google Scholar
  5. 5.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press (1996)Google Scholar
  6. 6.
    Freitas, A.A.: On rule interestingness measures. Knowledge-Based Systems 12(5-6), 309–315 (1999)CrossRefGoogle Scholar
  7. 7.
    Gavrilov, M., Anguelov, D., Indyk, P., Motwani, R.: Mining the stock market: which measure is best? In: KDD 2000, Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining, Boston, MA, USA, pp. 487–496 (2000)Google Scholar
  8. 8.
    Hilderman, R.J., Hamilton, H.J.: Knowledge Discovery and Measures of Interestingness. Kluwer Academic Publishers, Dordrecht (2001)CrossRefGoogle Scholar
  9. 9.
    Huynh, X.H., Guillet, F., Briand, H.: ARQAT: an exploratory analysis tool for interestingness measures. In: ASMDA 2005, Proceedings of the 11th International Symposium on Applied Stochastic Models and Data Analysis, Brest, France, pp. 334–344 (2005)Google Scholar
  10. 10.
    Huynh, X.H., Guillet, F., Briand, H.: Clustering interestingness measures with positive correlation. In: ICEIS 2005, Proceedings of the 7th International Conference on Enterprise Information Systems, Miami, USA, pp. 248–253 (2005)Google Scholar
  11. 11.
    Kononenco, I.: On biases in estimating multi-valued attributes. In: IJCAI 1995, Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Montreal, Canada, pp. 1034–1040 (1995)Google Scholar
  12. 12.
    Liu, B., Hsu, W., Mun, L., Lee, H.: Finding interesting patterns using user expectations. IEEE Transactions on Knowledge and Data Mining, IEEE Educational Activities Department 11(6), 817–832 (1999)Google Scholar
  13. 13.
    Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: [UCI] Repository of machine learning databases, University of California, Irvine, Department of Information and Computer Sciences (1998),
  14. 14.
    Padmanabhan, B., Tuzhilin, A.: A belief-driven method for discovering unexpected patterns. In: KDD 1998, Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, pp. 94–100. ACM Press, New York (1998)Google Scholar
  15. 15.
    Piatetsky-Shapiro, G.: Discovery, analysis and presentation of strong rules. In: Piatetsky-Shapiro, G., Frawley, W. (eds.) Knowledge Discovery in Databases, pp. 229–248. MIT Press, Cambridge (1991)Google Scholar
  16. 16.
    Ross, S.M.: Introduction to probability and statistics for engineers and scientists. Wiley, Chichester (1987)MATHGoogle Scholar
  17. 17.
    Tan, P.N., Kumar, V., Srivastava, J.: Selecting the right objective measure for association analysis. Information Systems 29(4), 293–313 (2004)CrossRefGoogle Scholar
  18. 18.
    Vaillant, B., Lenca, P., Lallich, S.: A clustering of interestingness measures. In: Suzuki, E., Arikawa, S. (eds.) DS 2004. LNCS (LNAI), vol. 3245, pp. 290–297. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xuan-Hiep Huynh
    • 1
  • Fabrice Guillet
    • 1
  • Henri Briand
    • 1
  1. 1.LINA CNRS 2729 – Polytechnic School of Nantes UniversityNantesFrance

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