Advertisement

Ramp: High Performance Frequent Itemset Mining with Efficient Bit-Vector Projection Technique

  • Shariq Bashir
  • Abdul Rauf Baig
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)

Abstract

Mining frequent itemset using bit-vector representation approach is very efficient for small dense datasets, but highly inefficient for sparse datasets due to lack of any efficient bit-vector projection technique. In this paper we present a novel efficient bit-vector projection technique, for sparse and dense datasets. We also present a new frequent itemset mining algorithm Ramp ( Real Algorithm for Mining Patterns) using bit-vector representation approach and our bit-vector projection technique. The performance of the Ramp is compared with the current best frequent itemset mining algorithms. Different experimental results on sparse datasets show that mining frequent itemset using Ramp is faster than the current best algorithms.

Keywords

Association Rule Pattern Mining Frequent Itemset Frequent Itemset Mining Dense Dataset 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB 1994, Santiago, Chile (1994)Google Scholar
  2. 2.
    Borgelt, C.: Efficient Implementation of Eclat and Apriori. In: IEEE ICDM 2003 Workshop FIMI 2003, Melbourne, Florida, USA (2003)Google Scholar
  3. 3.
    Burdick, D., Calimlim, M., Gehrke, J.: MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases. In: ICDE 2001, Heidelberg, Germany (2001)Google Scholar
  4. 4.
    Burdick, D., Calimlim, M., Flannick, J., Gehrke, J., Yiu, T.: MAFIA: A Performance Study of Mining Maximal Frequent Itemsets. In: IEEE ICDM 2003 Workshop FIMI 2003, Melbourne, Florida, USA (2003)Google Scholar
  5. 5.
    Fiat, A., Shporer, S.: AIM: Another Itemset Miner. In: IEEE ICDM 2003 Workshop FIMI 2003, Melbourne, Florida, USA (2003)Google Scholar
  6. 6.
    Grahne, G., Zhu, J.: Efficiently Using Prefix-trees in Mining Frequent Itemsets. In: IEEE ICDM 2003 Workshop FIMI 2003, Melbourne, Florida, USA (2003)Google Scholar
  7. 7.
    Goethals, B., Zaki, M.J. (eds.) Proc. IEEE ICDM Workshop Frequent Itemset Mining Implementations. CEUR Workshop Proc., vol. 80 (November 2003), http://CEUR-WS.org/Vol-90
  8. 8.
    Liu, G., Lu, H., Yu, J.X., Wei, W., Xiao, X.: AFOPT: An Efficient Implementation of Pattern Growth Approach. In: IEEE ICDM 2003 Workshop FIMI 2003, Melbourne, Florida, USA (2003)Google Scholar
  9. 9.
    Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., Yang, D.: H-Mine: Hyper-structure mining of frequent patterns in large databases. In: ICDM 2001, San Jose, California, USA (2001)Google Scholar
  10. 10.
    Pietracaprina, A., Zandolin, D.: Mining Frequent Itemsets using Patricia Tries. In: IEEE ICDM 2003 Workshop FIMI 2003, Melbourne, Florida, USA (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shariq Bashir
    • 1
  • Abdul Rauf Baig
    • 1
  1. 1.FAST-National University of Computer and Emerging SciencesIslamabadPakistan

Personalised recommendations