Mining Frequent Itemsets with Category-Based Constraints

  • Tien Dung Do
  • Siu Cheung Hui
  • Alvis Fong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2843)


The discovery of frequent itemsets is a fundamental task of association rule mining. The challenge is the computational complexity of the itemset search space. One of the solutions for this is to use constraints to focus on some specific itemsets. In this paper, we propose a specific type of constraints called category-based as well as the associated algorithm for constrained rule mining based on Apriori. The Category-based Apriori algorithm reduces the computational complexity of the mining process by bypassing most of the subsets of the final itemsets. An experiment has been conducted to show the efficiency of the proposed technique.


Association Rule Rule Mining Frequent Itemsets Association Rule Mining Support Threshold 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. of the 20th Int’l Conf. on Very Large Databases (VLDB 1994), Santiago, Chile, June 1994, pp. 487–499 (1994)Google Scholar
  2. 2.
    Sarasere, A., Omiecinsky, E., Navathe, S.: An efficient algorithm for mining association rules in large databases. In: 21st Int’l Conf. on Very Large Databases (VLDB), ZTrich, Switzerland, September 1995, pp. 432–444 (1995)Google Scholar
  3. 3.
    Hegland, M.: Algorithms for association rules. In: Mendelson, S., Smola, A.J. (eds.) Advanced Lectures on Machine Learning. LNCS (LNAI), vol. 2600, pp. 226–234. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Srikant, R., Vu, Q., Agrawal, R.: Mining Association Rules with Item Constraints. In: Proc. of the 3rd Int’l Conference on Knowledge Discovery in Databases and Data Mining, New-port Beach, California, August 1997, pp. 67–73 (1997)Google Scholar
  5. 5.
    Ng, R., Lakshmanan, L.V.S., Han, J., Pang, A.: Exploratory mining and pruning optimizations of constrained association rules. In: Proc. of SIGMOD, pp. 13–24 (1998)Google Scholar
  6. 6.
    Pei, J., Han, J., Lakshmanan, L.V.S.: Mining frequent itemsets with convertible constraints. In: Proc. of ICDE, pp. 433–442 (2001)Google Scholar
  7. 7.
    Lakshmanan, L.V.S., Ng, R., Han, J., Pang, A.: Optimization of constrained frequent set queries with 2-variable constraints. In: Proc. of SIGMOD, pp. 157–168 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Tien Dung Do
    • 1
  • Siu Cheung Hui
    • 1
  • Alvis Fong
    • 1
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore

Personalised recommendations