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A Novel Incremental Algorithm for Frequent Itemsets Mining in Dynamic Datasets

  • Raudel Hernández-León
  • José Hernández-Palancar
  • J. A. Carrasco-Ochoa
  • J. Fco. Martínez-Trinidad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)

Abstract

Frequent Itemsets (FI) Mining is one of the most researched areas of data mining. When some new transactions are appended, deleted or modified in a dataset, updating FI is a nontrivial task since such updates may invalidate existing FI or introduce new ones. In this paper a novel algorithm suitable for FI mining in dynamic datasets named Incremental Compressed Arrays is presented. In the experiments, our algorithm was compared against some algorithms as Eclat, PatriciaMine and FP-growth when new transactions are added or deleted.

Keywords

Data mining Frequent itemsets Dynamic datasets 

References

  1. 1.
    Agrawal, R., Shrikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th VLDB Conference, pp. 487–499 (1994)Google Scholar
  2. 2.
    Pietracaprina, A., Zandolin, D.: Mining Frequent Itemsets using Patricia Tries. In: Proceedings of the ICDM, Workshop on Frequent Itemset Mining Implementations, Melbourne, Florida, USA (2003)Google Scholar
  3. 3.
    A C++ Frequent Itemset Mining Template Library, http://www.cs.bme.hu/~bodon/en/index.html
  4. 4.
    Cheung, D., Han, J., Ng, V., Wong, C.Y.: Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique. In: Proceedings of the 12th Intl. Conf. on Data Engineering (1996)Google Scholar
  5. 5.
    Cheung, D., Lee, S., Kao, B.: A General Incremental Technique for Maintaining Discovered Association Rules. In: Proceedings of the 15th Intl. Conf. on Database Systems for Advanced Applications (1997)Google Scholar
  6. 6.
    Zaki, M., Parthasarathy, S., Ogihara, M., Li, W.: New Algorithms for Fast Discovery of Association Rules. Technical Report 651, The University of Rochester, New York, USA (1997)Google Scholar
  7. 7.
    Veloso, A., Meira Jr., W., de Carvalho, M.B., Possas, B., Parthasarathy, S., Zaki, M.: Mining Frequent Itemsets in Evolving Databases. In: Proceedings of the 2nd SIAM Intl. Conf. on Data Mining, Arlington, USA (2002)Google Scholar
  8. 8.
    Veloso, A., Gusmão, W., Meira Jr., W., de Carvalho, M.B., Parthasarathy, S., Zaki, M.: Parallel, Incremental and Interactive Mining for Frequent Itemsets in Evolving Databases. In: Intl. Workshop on High Performance Data Mining: Pervasive and Data Stream Mining (2003)Google Scholar
  9. 9.
    Cheung, W., Zaiane, O.R.: Incremental mining of frequent patterns without candidate generation or support constraint. In: Proceedings of the Seventh IEEE International Database Engineering and Applications Symposium, pp. 111–116 (2003)Google Scholar
  10. 10.
    Leung, C.K., Quamrul, I.K., Hoque, T.: CanTree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns. In: Proceedings of the Fifth IEEE International Conference on Data Mining (2005)Google Scholar
  11. 11.
    Hai, T.H., Shi, L.Z.: A New Method for Incremental Updating Frequent Patterns Mining. In: Proceedings of the Second International Conference on Innovative Computing, Informatio and Control, p. 561 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Raudel Hernández-León
    • 1
    • 2
  • José Hernández-Palancar
    • 1
  • J. A. Carrasco-Ochoa
    • 2
  • J. Fco. Martínez-Trinidad
    • 2
  1. 1.Advanced Technologies Application Center (CENATAV)La HabanaCuba
  2. 2.Computer Science Department National Institute of AstrophysicsOptics and ElectronicsPueblaMexico

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