TRARM-RelSup: Targeted Rare Association Rule Mining Using Itemset Trees and the Relative Support Measure

  • Jennifer Lavergne
  • Ryan Benton
  • Vijay V. Raghavan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7661)


The goal of association mining is to find potentially interesting rules in large repositories of data. Unfortunately using a minimum support threshold, a standard practice to improve the association mining processing complexity, can allow some of these rules to remain hidden. This occurs because not all rules which have high confidence have a high support count. Various methods have been proposed to find these low support rules, but the resulting increase in complexity can be prohibitively expensive. In this paper, we propose a novel targeted association mining approach to rare rule mining using the itemset tree data structure (aka TRARM-RelSup). This algorithm combines the efficiency of targeted association mining querying with the capabilities of rare rule mining; this results in discovering a more focused, standard and rare rules for the user, while keeping the complexity manageable.


data mining association mining targeted association mining itemset tree rare association rule mining 


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  1. 1.
    Adda, M., Wu, L., Feng, Y.: Rare Itemset Mining Machine Learning and Applications. In: ICMLA 2007, pp. 73–80 (2007)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)Google Scholar
  3. 3.
    Bing, L., Wynne, H., Yiming, M.: Mining association rules with multiple minimum supports. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 337–341 (1999)Google Scholar
  4. 4.
    Cooper, C., Zito, M.: Realistic synthetic data for testing association rule mining algorithms for market basket databases. In: Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 398–405 (2007)Google Scholar
  5. 5.
    Gedikli, F., Jannach, D.: Neighborhood-Restricted Mining and Weighted Application of Association Rules for Recommenders. In: Chen, L., Triantafillou, P., Suel, T. (eds.) WISE 2010. LNCS, vol. 6488, pp. 157–165. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Hafez, A., Deogun, J., Raghavan, V.V.: The Item-Set Tree: A Data Structure for Data Mining. In: Mohania, M., Tjoa, A.M. (eds.) DaWaK 1999. LNCS, vol. 1676, pp. 183–192. Springer, Heidelberg (1999)Google Scholar
  7. 7.
    Kiran, R., Reddy, P.: An improved multiple minimum support based approach to mine rare association rules. In: Computational Intelligence and Data Mining, CIDM 2009, pp. 340–347 (2009)Google Scholar
  8. 8.
    Kubat, M., Hafez, A., Raghavan, V., Lekkala, J., Chen, W.: Itemset trees for targeted association querying. IEEE Transactions on Knowledge and Data Engineering, 1522–1534 (2003)Google Scholar
  9. 9.
    Li, Y., Kubat, M.: Searching for high-support itemsets in itemset trees. Intell. Data Anal., 105–120 (2006)Google Scholar
  10. 10.
    Sha, Z., Chen, J.: Mining association rules from dataset containing predetermined decision itemset and rare transactions. In: Seventh International Conference on Natural Computation, vol. 1, pp. 166–170 (2011)Google Scholar
  11. 11.
    Srikant, R., Agrawal, R.: Mining generalized association rules. In: Proceedings of the 21st International Conference on Very Large Data Bases, VLDB 1995, pp. 407–419 (1995)Google Scholar
  12. 12.
    Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. SIGMOD Rec., 1–12 (1994)Google Scholar
  13. 13.
    Szathmary, L., Napoli, A., Valtchev, P.: Towards rare itemset mining. Tools with Artificial Intelligence, 305–312 (2007)Google Scholar
  14. 14.
    Yun, H., Ha, D., Hwang, B., Ryu, K.: Mining association rules on significant rare data using relative support. Journal of Systems and Software 67, 181–191 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jennifer Lavergne
    • 1
  • Ryan Benton
    • 1
  • Vijay V. Raghavan
    • 1
  1. 1.The Center for Advanced Computer StudiesUniversity of Louisiana at LafayetteLafayetteUSA

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