Efficient Parallel Mining of Gradual Patterns on Multicore Processors

  • Anne Laurent
  • Benjamin Négrevergne
  • Nicolas Sicard
  • Alexandre Termier
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 398)

Abstract

Mining gradual patterns plays a crucial role in many real world applications where huge volumes of complex numerical data must be handled, e.g., biological databases, survey databases, data streams or sensor readings. Gradual patterns highlight complex order correlations of the form “The more/less X, the more/less Y”. Only recently algorithms have appeared to mine efficiently gradual rules. However, due to the complexity of mining gradual rules, these algorithms cannot yet scale on huge real world datasets. In this paper, we thus propose to exploit parallelism in order to enhance the performances of the fastest existing one (GRITE) on multicore processors. Through a detailed experimental study, we show that our parallel algorithm scales very well with the number of cores available.

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References

  1. [Agrawal and Shafer, 1996]
    Agrawal, R., Shafer, J.C.: Parallel mining of association rules. IEEE Trans. Knowl. Data Eng. 8(6), 962–969 (1996)CrossRefGoogle Scholar
  2. [Agrawal and Srikant, 1994]
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB Conference, pp. 487–499 (1994)Google Scholar
  3. [Berzal et al., 2007]
    Berzal, F., Cubero, J.-C., Sanchez, D., Vila, M.-A., Serrano, J.M.: An alternative approach to discover gradual dependencies. Int. Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS) 15(5), 559–570 (2007)MathSciNetMATHCrossRefGoogle Scholar
  4. [Buehrer et al., 2006]
    Buehrer, G., Parthasarathy, S., Chen, Y.-K.: Adaptive parallel graph mining for cmp architectures. In: ICDM, pp. 97–106 (2006)Google Scholar
  5. [Di Jorio et al., 2008]
    Di Jorio, L., Laurent, A., Teisseire, M.: Fast extraction of gradual association rules: A heuristic based method. In: IEEE/ACM Int. Conf. on Soft computing as Transdisciplinary Science and Technology, CSTST 2008 (2008)Google Scholar
  6. [Di Jorio et al., 2009]
    Di Jorio, L., Laurent, A., Teisseire, M.: Mining Frequent Gradual Itemsets from Large Databases. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 297–308. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. [Fiot et al., 2008]
    Fiot, C., Masseglia, F., Laurent, A., Teisseire, M.: Gradual trends in fuzzy sequential patterns. In: Proc. of the Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU), Malaga, Spain, pp. 456–463 (2008)Google Scholar
  8. [Han and Kamber, 2006]
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. The Morgan Kaufmann Series in Data Management Systems, 2nd edn. Morgan Kaufmann, San Francisco (2006)Google Scholar
  9. [Han et al., 2000]
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: SIGMOD’00: Proceedings of the International Conference on Management of Data, Dallas, USA, pp. 1–12 (2000)Google Scholar
  10. [Hüllermeier, 2002]
    Hüllermeier, E.: Association Rules for Expressing Gradual Dependencies. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 200–211. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. [Laurent et al., 2009]
    Laurent, A., Lesot, M.-J., Rifqi, M.: Graank: Exploiting Rank Correlations for Extracting Gradual Dependencies. In: Andreasen, T., Yager, R.R., Bulskov, H., Christiansen, H., Larsen, H.L. (eds.) FQAS 2009. LNCS, vol. 5822, pp. 382–393. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. [Liu et al., 2007]
    Liu, L., Li, E., Zhang, Y., Tang, Z.: Optimization of frequent itemset mining on multiple-core processor. In: VLDB 2007: Proceedings of the 33rd International Conference on Very Large Data Bases VLDB Endowment, pp. 1275–1285 (2007)Google Scholar
  13. [Lucchese et al., 2007]
    Lucchese, C., Orlando, S., Perego, R.: Parallel mining of frequent closed patterns: Harnessing modern computer architectures. In: ICDM, Omaha, USA, pp. 242–251 (2007)Google Scholar
  14. [Masseglia et al., 2004]
    Masseglia, F., Poncelet, P., Teisseire, M.: Pre-processing time constraints for efficiently mining generalized sequential patterns. In: International Syposium on Temporal Representation and Reasoning, pp. 87–95. IEEE Computer Society Press (2004)Google Scholar
  15. [Pasquier et al., 1999]
    Pasquier, N., Yves, B.Y., Taouil, R., Lakhal, L.: Efficient mining of association rules using closed itemset lattices. Information Systems 24, 25–46 (1999)CrossRefGoogle Scholar
  16. [Tatikonda and Parthasarathy, 2009]
    Tatikonda, S., Parthasarathy, S.: Mining tree-structured data on multicore systems. In: VLDB 2009: Proceedings of the 35th International Conference on Very Large Data Bases, Lyon, France, pp. 694–705 (2009)Google Scholar
  17. [Uno, 2005]
    Uno, T.: Lcm ver. 3: Collaboration of array, bitmap and prefix tree for frequent itemset mining. In: In Proc. of the ACM SIGKDD Open Source Data Mining Workshop on Frequent Pattern Mining Implementations, Chicago, USA, pp. 77–86 (2005)Google Scholar
  18. [Yan and Han, 2002]
    Yan, X., Han, J.: gspan: Graph-based substructure pattern mining. In: ICDM 2002: Proceedings of the 2002 IEEE International Conference on Data Mining, p. 721. IEEE Computer Society, Washington, DC, USA (2002)Google Scholar
  19. [Zaki, 1999]
    Zaki, M.J.: Parallel sequence mining on shared-memory machines. In: KDD Conference on Large-Scale Parallel KDD Systems Workshop, San-Diego, USA, pp. 161–189 (1999)Google Scholar
  20. [Zaki et al., 1997]
    Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: Parallel algorithms for discovery of association rules. Data Min. Knowl. Discov. 1(4), 343–373 (1997)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Anne Laurent
    • 1
  • Benjamin Négrevergne
    • 2
  • Nicolas Sicard
    • 3
  • Alexandre Termier
    • 2
  1. 1.Univ. Montpellier 2, LIRMM, CNRS UMR 5506Montpellier cedex 5France
  2. 2.LIG, UJF, CNRS UMR 5217Saint Martin d’HèresFrance
  3. 3.LRIE, EFREIVillejuifFrance

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