FSSM: Fast Construction of the Optimized Segment Support Map

  • Kok-Leong Ong
  • Wee-Keong Ng
  • Ee-Peng Lim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2737)


Computing the frequency of a pattern is one of the key operations in data mining algorithms. Recently, the Optimized Segment Support Map (OSSM) was introduced as a simple but powerful way of speeding up any form of frequency counting satisfying the monotonicity condition. However, the construction cost to obtain the ideal OSSM is high, and makes it less attractive in practice. In this paper, we propose the FSSM, a novel algorithm that constructs the OSSM quickly using a FP-Tree. Given a user-defined segment size, the FSSM is able to construct the OSSM at a fraction of the time required by the algorithm previously proposed. More importantly, this fast construction time is achieved without compromising the quality of the OSSM. Our experimental results confirm that the FSSM is a promising solution for constructing the best OSSM within user given constraints.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Srikant, R.: Fast Algorithm for Mining Association Rules. In: Proc. of VLDB, Santiago, Chile, pp. 487–499 (August 1994)Google Scholar
  2. 2.
    Cai, C.H., Fu, A.W.C., Cheng, C.H., Kwong, W.W.: Mining Association Rules with Weighted Items. In: Proc. of IDEAS Symp. (August 1998)Google Scholar
  3. 3.
    Dong, G., Li, J.: Efficient Mining of Emerging Patterns: Discovering Trends and Differences. In: Proc. of ACM SIGKDD, San Diego, CA, USA (August 1999)Google Scholar
  4. 4.
    Han, J., Fu, Y.: Discovery of Multiple-Level Association Rules from Large Databases. In: Proc. of VLDB, Zurich, Swizerland (1995)Google Scholar
  5. 5.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining Frequent Patterns without Candidate Generation: A Frequent-pattern Tree Approach. J. of Data Mining and Knowledge Discovery 7(3/4) (2003)Google Scholar
  6. 6.
    Kohavi, R., Brodley, C., Frasca, B., Mason, L., Zheng, Z.: KDD-Cup 2000 organizers’ report: Peeling the onion. SIGKDD Explorations  2(2), 86–98 (2000)Google Scholar
  7. 7.
    Koperski, K., Han, J.: Discovery of Spatial Association Rules in Geographic Information Databases. In: Proc. of the 14th Int. Symp. on Large Spatial Databases, Maine (August 1995)Google Scholar
  8. 8.
    Lakshmanan, L., Leung, K.-S., Ng, R.T.: The Segment Support Map: Scalable Mining of Frequent Itemsets. SIGKDD Explorations 2, 21–27 (2000)CrossRefGoogle Scholar
  9. 9.
    Mannila, H., Toivonen, H., Verkamo, A.I.: Discovering Frequent Episodes in Sequences. In: Proc. of ACM SIGKDD, Montreal, Canada (August 1995)Google Scholar
  10. 10.
    Leung, C.K.-S., Ng, R.T., Mannila, H.: OSSM: A Segmentation Approach to Optimize Frequency Counting. In: Proc. of IEEE Int. Conf. on Data Engineering, San Jose, CA, USA, pp. 583–592 (February 2002)Google Scholar
  11. 11.
    Ng, R.T., Lakshmanan, L.V.S., Han, J.: Exploratory Mining and Pruning Optimizations of Constrained Association Rules. In: Proc. of SIGMOD, Washington, USA (June 1998)Google Scholar
  12. 12.
    Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Proc. of the 5th Int. Conf. on Extending Database Technology, Avignon, France (March 1996)Google Scholar
  13. 13.
    Zaiane, O.R., Han, J., Zhu, H.: Mining Recurrent Items in Multimedia with Progressive Resolution Refinement. In: Proc. of ICDE, San Diego (March 2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Kok-Leong Ong
    • 1
  • Wee-Keong Ng
    • 1
  • Ee-Peng Lim
    • 1
  1. 1.Centre for Advanced Information SystemsNanyang Technological UniversitySingaporeSingapore

Personalised recommendations