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An Efficient Framework for Mining Flexible Constraints

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Advances in Knowledge Discovery and Data Mining (PAKDD 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3518))

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Abstract

Constraint-based mining is an active field of research which is a key point to get interactive and successful KDD processes. Nevertheless, usual solvers are limited to particular kinds of constraints because they rely on properties to prune the search space which are incompatible together. In this paper, we provide a general framework dedicated to a large set of constraints described by SQL-like and syntactic primitives. This set of constraints covers the usual classes and introduces new tough and flexible constraints. We define a pruning operator which prunes the search space by automatically taking into account the characteristics of the constraint at hand. Finally, we propose an algorithm which efficiently makes use of this framework. Experimental results highlight that usual and new complex constraints can be mined in large datasets.

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References

  1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. 20th Int. Conf. Very Large Data Bases, VLDB, pp. 432–444 (1994)

    Google Scholar 

  2. Bonchi, F., Lucchese, C.: On closed constrained frequent pattern mining. In: Proceedings of ICDM 2004, pp. 35–42 (2004)

    Google Scholar 

  3. Boulicaut, J.F., Bykowski, A., Rigotti, C.: Free-sets: a condensed representation of boolean data for the approximation of frequency queries. Data Mining and Knowledge Discovery journal 7(1), 5–22 (2003)

    Article  MathSciNet  Google Scholar 

  4. Calders, T., Goethals, B.: Minimal k-free representations of frequent sets. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 71–82. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  5. De Raedt, L., Jäger, M., Lee, S.D., Mannila, H.: A theory of inductive query answering. In: Proceedings of ICDM 2002, Maebashi, Japan, pp. 123–130 (2002)

    Google Scholar 

  6. Dong, G., Li, J.: Efficient mining of emerging patterns: Discovering trends and differences. In: Knowledge Discovery and Data Mining, pp. 43–52 (1999)

    Google Scholar 

  7. Gade, K., Wang, J., Karypis, G.: Efficient closed pattern mining in the presence of tough block constraints. In: Proceedings of ACM SIGKDD, pp. 138–147 (2004)

    Google Scholar 

  8. Imielinski, T., Mannila, H.: A database perspective on knowledge discovery. In: Communication of the ACM, pp. 58–64 (1996)

    Google Scholar 

  9. Jeudy, B., Rioult, F.: Database transposition for constrained (closed) pattern mining. In: Goethals, B., Siebes, A. (eds.) KDID 2004. LNCS, vol. 3377, pp. 89–107. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  10. Kiefer, D., Gehrke, J., Bucila, C., White, W.: How to quickly find a witness. In: Proceedings of ACM SIGMOD/PODS 2003 Conference, pp. 272–283 (2003)

    Google Scholar 

  11. Kryszkiewicz, M.: Inferring knowledge from frequent patterns. In: Bustard, D.W., Liu, W., Sterritt, R. (eds.) Soft-Ware 2002. LNCS, vol. 2311, pp. 247–262. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  12. Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery 1(3), 241–258 (1997)

    Article  Google Scholar 

  13. Ng, R.T., Lakshmanan, L.V.S., Han, J.: Exploratory mining and pruning optimizations of constrained associations rules. In: Proceedings of SIGMOD (1998)

    Google Scholar 

  14. Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. LNCS. Springer, Heidelberg (1999)

    Google Scholar 

  15. Pei, J., Han, J., Lakshmanan, L.V.S.: Mining frequent item sets with convertible constraints. In: Proceedings of ICDE, pp. 433–442 (2001)

    Google Scholar 

  16. Soulet, A., Crémilleux, B., Rioult, F.: Condensed representation of emerging patterns. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 127–132. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  17. Soulet, A., Crémilleux, B.: A general framework designed for constraint-based mining. Technical report, Université de Caen, Caen, France (2004)

    Google Scholar 

  18. Wang, K., Jiang, Y., Yu, J.X., Dong, G., Han, J.: Pushing aggregate constraints by divide-and-approximate. In: Proceedings of ICDE, pp. 291–302 (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. GREYC, CNRS – UMR 6072, Université de Caen, Campus Côte de Nacre, F-14032, Caen Cédex, France

    Arnaud Soulet & Bruno Crémilleux

Authors
  1. Arnaud Soulet
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  2. Bruno Crémilleux
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Editor information

Editors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Asahidai 1-1, 923-12292, Nomi, Japan

    Tu Bao Ho

  2. University of Hong Kong, Pokfulam Road, Hong Kong, China

    David Cheung

  3. Department of Computer Science and Engineering, Arizona State University, Tempe, Arizona, USA

    Huan Liu

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© 2005 Springer-Verlag Berlin Heidelberg

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Soulet, A., Crémilleux, B. (2005). An Efficient Framework for Mining Flexible Constraints. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_76

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  • DOI: https://doi.org/10.1007/11430919_76

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26076-9

  • Online ISBN: 978-3-540-31935-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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