Mining Frequent Itemsets from Uncertain Data

  • Chun-Kit Chui
  • Ben Kao
  • Edward Hung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4426)


We study the problem of mining frequent itemsets from uncertain data under a probabilistic framework. We consider transactions whose items are associated with existential probabilities and give a formal definition of frequent patterns under such an uncertain data model. We show that traditional algorithms for mining frequent itemsets are either inapplicable or computationally inefficient under such a model. A data trimming framework is proposed to improve mining efficiency. Through extensive experiments, we show that the data trimming technique can achieve significant savings in both CPU cost and I/O cost.


Association Rule Frequent Itemsets Uncertain Data Support Threshold Support Count 
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 large databases. In: Proc. of 20th International Conference on Very Large Data Bases, Santiago de Chile, Chile, September 12-15, 1994, pp. 487–499. Morgan Kaufmann, San Francisco (1994)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proc. of the 11th International Conference on Data Engineering, Taipei, Taiwan, March 6-10, 1995, pp. 3–14. IEEE Computer Society Press, Los Alamitos (1995)Google Scholar
  3. 3.
    Dai, X., et al.: Probabilistic spatial queries on existentially uncertain data. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 400–417. Springer, Heidelberg (2005)Google Scholar
  4. 4.
    Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: KDD 1998, pp. 80–86 (1998)Google Scholar
  5. 5.
    Rushing, A., et al.: Using association rules as texture features. IEEE Trans. Pattern Anal. Mach. Intell. 23(8), 845–858 (2001)CrossRefGoogle Scholar
  6. 6.
    Zimányi, E., Pirotte, A.: Imperfect information in relational databases. In: Uncertainty Management in Information Systems, pp. 35–88 (1996)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Chun-Kit Chui
    • 1
  • Ben Kao
    • 1
  • Edward Hung
    • 2
  1. 1.Department of Computer Science, The University of Hong Kong, PokfulamHong Kong
  2. 2.Department of Computing, Hong Kong Polytechnic University, KowloonHong Kong

Personalised recommendations