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Pushing Constraints to Detect Local Patterns

  • Conference paper
Local Pattern Detection

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3539))

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Abstract

The main position of this paper is that constraints can be a very useful tool in the search for local patterns. The justification for our position is twofold. On one hand, pushing constraints makes feasible the computation of frequent patterns at very low frequency levels, which is where local patterns are. On the other hand constraints can be exploited to guide the search for those patterns showing deviating, surprising characteristics. We first review the many definitions of local patterns. This review leads us to justify our position. We then provide a survey of techniques for pushing constraint into the frequent pattern computation.

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References

  1. Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM International Conference on Management of Data, SIGMOD 1993 (1993)

    Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proceedings of the Twentieth International Conference on Very Large Databases, VLDB 1994 (1994)

    Google Scholar 

  3. Bonchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: Adaptive Constraint Pushing in frequent pattern mining. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 47–58. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  4. Bonchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: ExAMiner: Optimized level-wise frequent pattern mining with monotone constraints. In: Proceedings of the Third IEEE International Conference on Data Mining, ICDM 2003 (2003)

    Google Scholar 

  5. Bonchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: ExAnte: Anticipated data reduction in constrained pattern mining. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 59–70. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  6. Bonchi, F., Goethals, B.: FP-bonsai: The art of growing and pruning small FP-trees. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 155–160. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  7. Bonchi, F., Lucchese, C.: On closed constrained frequent pattern mining. In: Proceedings of the Fourth IEEE International Conference on Data Mining, ICDM 2004 (2004)

    Google Scholar 

  8. Bonchi, F., Lucchese, C.: Pushing tougher constraints in frequent pattern mining. Technical Report 2004-TR-63, ISTI-C.N.R (2004) (Submitted to SDM 2005)

    Google Scholar 

  9. Bucila, C., Gehrke, J., Kifer, D., White, W.: DualMiner: A dual-pruning algorithm for itemsets with constraints. In: Proceedings of the 8th ACM International Conference on Knowledge Discovery and Data Mining, SIGKDD 2002 (2002)

    Google Scholar 

  10. Flach, P., et al.: On the road to knowledge: mining 21 years of uk traffic accident reports. In: Data Mining and Decision Support: Aspects of Integration and Collaboration, January 2003, pp. 143–155. Kluwer Academic Publishers, Dordrecht (2003)

    Google Scholar 

  11. Grahne, G., Lakshmanan, L., Wang, X.: Efficient mining of constrained correlated sets. In: 16th IEEE International Conference on Data Engineering, ICDE 2000 (2000)

    Google Scholar 

  12. Han, J., Lakshmanan, L.V.S., Ng, R.T.: Constraint-based, multidimensional data mining. Computer 32(8), 46–50 (1999)

    Article  Google Scholar 

  13. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM International Conference on Management of Data, SIGMOD 2000 (2000)

    Google Scholar 

  14. Hand, D.: Pattern detection and discovery. In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds.) Pattern Detection and Discovery. LNCS (LNAI), vol. 2447, p. 1. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  15. Hand, D.J.: Pattern Detection and Discovery. In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds.) Pattern Detection and Discovery. LNCS (LNAI), vol. 2447, p. 1. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  16. Jeudy, B., Boulicaut, J.-F.: Optimization of association rule mining queries. Intelligent Data Analysis Journal 6(4), 341–357 (2002)

    MATH  Google Scholar 

  17. Lakshmanan, L.V.S., Ng, R.T., Han, J., Pang, A.: Optimization of constrained frequent set queries with 2-variable constraints. In: Proceedings of the ACM International Conference on Management of Data, SIGMOD 1999 (1999)

    Google Scholar 

  18. Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on multiple class-association rules. In: Proceedings of the 2001 IEEE International Conference on Data Mining, ICDM 2001 (2001)

    Google Scholar 

  19. Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: 4th Int. Conf. Knowledge Discovery and Data Mining (KDD 1998), New York, pp. 80–86 (1998)

    Google Scholar 

  20. Mannila, H.: Local and global methods in data mining: Basic techniques and open problems. In: Widmayer, P., Triguero, F., Morales, R., Hennessy, M., Eidenbenz, S., Conejo, R. (eds.) ICALP 2002. LNCS, vol. 2380, p. 57. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  21. Ng, R.T., Lakshmanan, L.V.S., Han, J., Pang, A.: Exploratory mining and pruning optimizations of constrained associations rules. In: Proceedings of the ACM International Conference on Management of Data, SIGMOD 1998 (1998)

    Google Scholar 

  22. Ordonez, C., de Braal, L., Santana, C.A.: Discovering interesting association rules in medical data. In: ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD 2000 (2000)

    Google Scholar 

  23. Ordonez, C., Omiecinski, E., de Braal, L., Santana, C.A., Ezquerra, N., Taboada, J.A., Cooke, D., Krawczynska, E., Garcia, E.V.: Mining constrained association rules to predict heart disease. In: Proceedings of the First IEEE International Conference on Data Mining, ICDM 2001 (2001)

    Google Scholar 

  24. Pei, J., Han, J.: Can we push more constraints into frequent pattern mining? In: Proceedings of the 6th ACM International Conference on Knowledge Discovery and Data Mining, SIGKDD 2000 (2000)

    Google Scholar 

  25. Pei, J., Han, J., Lakshmanan, L.V.S.: Mining frequent item sets with convertible constraints. In: 17th IEEE International Conference on Data Engineering, ICDE 2001 (2001)

    Google Scholar 

  26. Pei, J., Zhang, X., Cho, M., Wang, H., Yu, P.: Maple: A fast algorithm for maximal pattern-based clustering. In: Proceedings of the Third IEEE International Conference on Data Mining, ICDM 2003 (2003)

    Google Scholar 

  27. Raedt, L.D., Kramer, S.: The levelwise version space algorithm and its application to molecular fragment finding. In: Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI 2001 (2001)

    Google Scholar 

  28. Srikant, R., Vu, Q., Agrawal, R.: Mining association rules with item constraints. In: Proceedings of the 3rd ACM International Conference on Knowledge Discovery and Data Mining, SIGKDD 1997 (1997)

    Google Scholar 

  29. Yiu, M.L., Mamoulis, N.: Frequent-pattern based iterative projected clustering. In: Proceedings of the Third IEEE International Conference on Data Mining, ICDM 2003 (2003)

    Google Scholar 

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Authors and Affiliations

  1. Pisa KDD Laboratory, ISTI – CNR, Area della Ricerca di Pisa, Via Giuseppe Moruzzi, 1, 56124, Pisa, Italy

    Francesco Bonchi & Fosca Giannotti

Authors
  1. Francesco Bonchi
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  2. Fosca Giannotti
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Editor information

Editors and Affiliations

  1. Computer Science VIII, artificial Intelligence Unit, Technische Universität Dortmund, 44221, Dortmund, Germany

    Katharina Morik

  2. INSA-Lyon, LIRIS CNRS UMR5205, F-69621, Villeurbanne, France

    Jean-François Boulicaut

  3. Department of Computer Science, Universiteit Utrecht,  

    Arno Siebes

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© 2005 Springer-Verlag Berlin Heidelberg

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Bonchi, F., Giannotti, F. (2005). Pushing Constraints to Detect Local Patterns. In: Morik, K., Boulicaut, JF., Siebes, A. (eds) Local Pattern Detection. Lecture Notes in Computer Science(), vol 3539. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504245_1

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  • DOI: https://doi.org/10.1007/11504245_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26543-6

  • Online ISBN: 978-3-540-31894-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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