Symmetric Item Set Mining Based on Zero-Suppressed BDDs

  • Shin-ichi Minato
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4265)


In this paper, we propose a method for discovering hidden information from large-scale item set data based on the symmetry of items. Symmetry is a fundamental concept in the theory of Boolean functions, and there have been developed fast symmetry checking methods based on BDDs (Binary Decision Diagrams). Here we discuss the property of symmetric items in data mining problems, and describe an efficient algorithm based on ZBDDs (Zero-suppressed BDDs). The experimental results show that our ZBDD-based symmetry checking method is efficiently applicable to the practical size of benchmark databases.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining Association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proc. of the 1993 ACM SIGMOD International Conference on Management of Data. SIGMOD Record, vol. 22(2), pp. 207–216. ACM Press, New York (1993)CrossRefGoogle Scholar
  2. 2.
    Bryant, R.E.: Graph-based algorithms for Boolean function manipulation. IEEE Trans. Comput. C-35(8), 677–691 (1986)Google Scholar
  3. 3.
    Goethals, B.: Survey on Frequent Pattern Mining, Manuscript (2003),
  4. 4.
    Goethals, B., Zaki, M.J. (eds.): Frequent Itemset Mining Dataset Repository. In: Frequent Itemset Mining Implementations (FIMI 2003) (2003),
  5. 5.
    Kettle, N., King, A.: An Anytime Symmetry Detection Algorithm for ROBDDs.’. In: Proc. IEEE/ACM 11th Asia and South Pacific Design Automation Conference (ASPDAC 2006), pp. 243–248 (January 2006)Google Scholar
  6. 6.
    Minato, S.: Zero-suppressed BDDs for set manipulation in combinatorial problems. In: Proc. 30th ACM/IEEE Design Automation Conf. (DAC 1993), pp. 272–277 (1993)Google Scholar
  7. 7.
    Minato, S., Arimura, H.: Efficient Combinatorial Item Set Analysis Based on Zero-Suppressed BDDs. In: IEEE/IEICE/IPSJ International Workshop on Challenges in Web Information Retrieval and Integration (WIRI 2005), pp. 3–10 (April 2005)Google Scholar
  8. 8.
    Minato, S.: Finding Simple Disjoint Decompositions in Frequent Itemset Data Using Zero-suppressed BDD’. In: Proc. of IEEE ICDM 2005 workshop on Computational Intelligence in Data Mining, pp. 3–11 (November 2005) ISBN-0-9738918-5-8Google Scholar
  9. 9.
    Mishchenko, A.: Fast Computation of Symmetries in Boolean Functions. IEEE Trans. Computer-Aided Design 22(11), 1588–1593 (2003)CrossRefGoogle Scholar
  10. 10.
    Uno, T., Asai, T., Uchida, Y., Arimura, H.: An efficient algorithm for enumerating closed patterns in transaction databases. In: Suzuki, E., Arikawa, S. (eds.) DS 2004. LNCS, vol. 3245, pp. 16–31. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shin-ichi Minato
    • 1
  1. 1.Graduate School of Information Science and TechnologyHokkaido UniversitySapporoJapan

Personalised recommendations