ExMiner: An Efficient Algorithm for Mining Top-K Frequent Patterns

  • Tran Minh Quang
  • Shigeru Oyanagi
  • Katsuhiro Yamazaki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


Conventional frequent pattern mining algorithms require users to specify some minimum support threshold. If that specified-value is large, users may lose interesting information. In contrast, a small minimum support threshold results in a huge set of frequent patterns that users may not be able to screen for useful knowledge. To solve this problem and make algorithms more user-friendly, an idea of mining the k-most interesting frequent patterns has been proposed. This idea is based upon an algorithm for mining frequent patterns without a minimum support threshold, but with a k number of highest frequency patterns. In this paper, we propose an explorative mining algorithm, called ExMiner, to mine k-most interesting (i.e. top-k) frequent patterns from large scale datasets effectively and efficiently. The ExMiner is then combined with the idea of “build once mine anytime” to mine top-k frequent patterns sequentially. Experiments on both synthetic and real data show that our proposed methods are more efficient compared to the existing ones.


Frequent Pattern Frequent Itemsets Support Threshold Explorative Mining Frequent Pattern Mining 
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 algorithm for mining association rules. In: Proc. of VLDB 1994, Santiago, Chille, pp. 487–499 (1994)Google Scholar
  2. 2.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. of ACM SIGMOD Conference on Management of Data, pp. 1–12 (2000)Google Scholar
  3. 3.
    Bayard, R.J.: Efficiently mining long patterns from databases. In: Proc. of ACM SIGMOD Conference on Management of Data, pp. 85–93 (1998)Google Scholar
  4. 4.
    Grahne, G., Zhu, J.: High performance mining of maxima frequent itemsets. In: Proc. of SIAM 2003 workshop on High Performance Data Mining (2003)Google Scholar
  5. 5.
    Grahne, G., Zhu, J.: Efficiently using prefix-tree in mining frequent itemsets. In: Proc. of IEEE ICDM workshop on Frequent Itemsets Mining Implementations (2003)Google Scholar
  6. 6.
    Pei, J., Han, J., Mao, R.: CLOSET: An efficient algorithm from mining frequent closed itemsets. In: Proc. of DMKD (2000)Google Scholar
  7. 7.
    Fu, A.W., Kwong, R.W., Tang, J.: Mining N most interesting itemsets. In: Ohsuga, S., Raś, Z.W. (eds.) ISMIS 2000. LNCS (LNAI), vol. 1932, Springer, Heidelberg (2000)Google Scholar
  8. 8.
    Han, J., Wang, J., Lu, Y., Tzvetkov, P.: Mining top-k frequent closed patterns without minimum support. In: Proc. of IEEE ICDM Conference on Data Mining (2002)Google Scholar
  9. 9.
    Ly, S., Hong, S., Paul, P., Rodney, T.: Finding the N largest itemsets. In: Proc. Int. Conf. on Data Mining, Rio de Janeiro, Brazil, pp. 211–222 (1998)Google Scholar
  10. 10.
    Wang, J., Han, J., Lu, Y., Tzvetkov, P.: TFP: An efficient algorithm for mining top-k frequent closed itemsets. Proc. of IEEE Knowledge and Data Engineering 17(5), 652–663 (2005)CrossRefGoogle Scholar
  11. 11.
    Hirate, Y., Iwahashi, E., Yamana, H.: TF2P-growth: An efficient algorithm for mining frequent patterns without any thresholds. In: Proc. of ICDM (2004)Google Scholar
  12. 12.
    IBM Quest Data Mining Project. Quest synthetic data generation,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tran Minh Quang
    • 1
  • Shigeru Oyanagi
    • 1
  • Katsuhiro Yamazaki
    • 1
  1. 1.Graduate school of Science and EngineeringRitsuimeikan UniversityKusatsu cityJapan

Personalised recommendations