Mining Top-K Association Rules

  • Philippe Fournier-Viger
  • Cheng-Wei Wu
  • Vincent S. Tseng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7310)


Mining association rules is a fundamental data mining task. However, depending on the choice of the parameters (the minimum confidence and minimum support), current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information.This is a serious problem because in practice users have limited resources for analyzing the results and thus are often only interested in discovering a certain amount of results, and fine tuning the parameters is time-consuming.To address this problem, we propose an algorithm to mine the top-k association rules, where k is the number of association rules to be found and is set by the user. The algorithm utilizes a new approach for generating association rules named rule expansions and includes several optimizations. Experimental results show that the algorithm has excellent performance and scalability, and that it is an advantageous alternative to classical association rule mining algorithms when the user want to control the number of rules generated.


association rule mining top-k rules rule expansion support 


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  1. 1.
    Agrawal, R., Imielminski, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. In: Proc. ACM Intern. Conf. on Management of Data, pp. 207–216. ACM Press (June 1993)Google Scholar
  2. 2.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann Publ., San Francisco (2006)zbMATHGoogle Scholar
  3. 3.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining Frequent Patterns without Candidate Generation. Data Mining and Knowledge Discovery 8, 53–87 (2004)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., Yang, D.: H-Mine: Fast and space-preserving frequent pattern mining in large databases. IIE Trans. 39(6), 593–605 (2007)CrossRefGoogle Scholar
  5. 5.
    Webb, G.I., Zhang, S.: K-Optimal-Rule-Discovery. Data Mining and Knowledge Discovery 10(1), 39–79 (2005)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Webb, G.I.: Filtered top-k association discovery. WIREs Data Mining and Knowledge Discovery 1, 183–192 (2011)CrossRefGoogle Scholar
  7. 7.
    Kun Ta, C., Huang, J.-L., Chen, M.-S.: Mining Top-k Frequent Patterns in the Presence of the Memory Constraint. VLDB Journal 17(5), 1321–1344 (2008)CrossRefGoogle Scholar
  8. 8.
    Wang, J., Lu, Y., Tzvetkov, P.: Mining Top-k Frequent Closed Itemsets. IEEE Trans. Knowledge and Data Engineering 17(5), 652–664 (2005)CrossRefGoogle Scholar
  9. 9.
    Pietracaprina, A., Vandin, F.: Efficient Incremental Mining of Top-K Frequent Closed Itemsets. In: Corruble, V., Takeda, M., Suzuki, E. (eds.) DS 2007. LNCS (LNAI), vol. 4755, pp. 275–280. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Tzvetkov, P., Yan, X., Han, J.: TSP: Mining Top-k Closed Sequential Patterns. Knowledge and Information Systems 7(4), 438–457 (2005)CrossRefGoogle Scholar
  11. 11.
    You, Y., Zhang, J., Yang, Z., Liu, G.: Mining Top-k Fault Tolerant Association Rules by Redundant Pattern Disambiguation in Data Streams. In: Proc. 2010 Intern. Conf. Intelligent Computing and Cognitive Informatics, pp. 470–473. IEEE Press (March 2010)Google Scholar
  12. 12.
    Cormen, T.H., Leiserson, C.E., Rivest, R., Stein, C.: Introduction to Algorithms, 3rd edn. MIT Press, Cambridge (2009)zbMATHGoogle Scholar
  13. 13.
    Lucchese, C., Orlando, S., Perego, R.: Fast and Memory Efficient Mining of Frequent Closed Itemsets. IEEE Trans. Knowl. and Data Eng. 18(1), 21–36 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Philippe Fournier-Viger
    • 1
  • Cheng-Wei Wu
    • 2
  • Vincent S. Tseng
    • 2
  1. 1.Dept. of Computer ScienceUniversity of MonctonCanada
  2. 2.Dept. of Computer Science and Information EngineeringNational Cheng Kung UniversityTaiwan

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