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Constrained Sequential Pattern Knowledge in Multi-relational Learning

  • Carlos Abreu Ferreira
  • João Gama
  • Vítor Santos Costa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7026)

Abstract

In this work we present XMuSer, a multi-relational framework suitable to explore temporal patterns available in multi-relational databases. XMuSer’s main idea consists of exploiting frequent sequence mining, using an efficient and direct method to learn temporal patterns in the form of sequences. Grounded on a coding methodology and on the efficiency of sequential miners, we find the most interesting sequential patterns available and then map these findings into a new table, which encodes the multi-relational timed data using sequential patterns. In the last step of our framework, we use an ILP algorithm to learn a theory on the enlarged relational database that consists on the original multi-relational database and the new sequence relation.

We evaluate our framework by addressing three classification problems. Moreover, we map each one of three different types of sequential patterns: frequent sequences, closed sequences or maximal sequences.

Keywords

Sequential Pattern Inductive Logic Programming Frequent Sequence Fact Table Mining Sequential Pattern 
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.

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References

  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann, Santiago de Chile (1994)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Eleventh International Conference on Data Engineering, Taipei, Taiwan, pp. 3–14 (1995)Google Scholar
  3. 3.
    de Amo, S., Furtado, D.A.: First-order temporal pattern mining with regular expression constraints. Data & Knowledge Engineering 62(3), 401–420 (2007); including special issue: 20th Brazilian Symposium on Databases (SBBD 2005)CrossRefGoogle Scholar
  4. 4.
    Blockeel, H., Sebag, M.: Scalability and efficiency in multi-relational data mining. SIGKDD Explorations 5(1), 17–30 (2003)CrossRefGoogle Scholar
  5. 5.
    Davis, J., Burnside, E., Ramakrishnan, R., Costa, V.S., Shavlik, J.: View learning for statistical relational learning: With an application to mammography. In: Proc. of the 19th International Joint Conference on Artificial Intelligence, Professional Book Center, Edinburgh, Scotland, UK, pp. 677–683 (2005)Google Scholar
  6. 6.
    Dehaspe, L., Toivonen, H.: Discovery of frequent DATALOG patterns. Data Mining and Knowledge Discovery 3(1), 7–36 (1999)CrossRefGoogle Scholar
  7. 7.
    Esposito, F., Di Mauro, N., Basile, T.M.A., Ferilli, S.: Multi-dimensional relational sequence mining. Fundamenta Informaticae 89(1), 23–43 (2009)zbMATHGoogle Scholar
  8. 8.
    Ferreira, C.A., Gama, J., Costa, V.S.: Sequential Pattern Mining in Multi-Relational Datasets. In: Meseguer, P., Mandow, L., Gasca, R.M. (eds.) CAEPIA 2009. LNCS, vol. 5988, pp. 121–130. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Garofalakis, M., Rastogi, R., Shim, K.: Mining sequential patterns with regular expression constraints. IEEE Transactions on Knowledge and Data Engineering 14(3), 530–552 (2002)CrossRefGoogle Scholar
  10. 10.
    Jian, P., Han, J., Mortazavi-asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: Proc. of the 17th International Conference on Data Engineering, pp. 215–224. IEEE Computer Society, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    Dan Lee, S., De Raedt, L.: Constraint Based Mining of First Order Sequences in SeqLog. In: Meo, R., Lanzi, P.L., Klemettinen, M. (eds.) Database Support for Data Mining Applications. LNCS (LNAI), vol. 2682, pp. 154–173. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Ohara, K., Yoshida, T., Geamsakul, W., Motoda, H., Washio, T., Yokoi, H., Takabayashi, K.: Analysis of Hepatitis Dataset by Decision Tree Graph-Based Induction. In: Proceedings of Discovery Challenge, pp. 173–184 (2004)Google Scholar
  13. 13.
    Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Heidelberg (1996)Google Scholar
  14. 14.
    Yan, X., Han, J., Afshar, R.: Clospan: Mining closed sequential patterns in large datasets. In: Proc. of the Third SIAM International Conference on Data Mining, pp. 166–177. SIAM, San Francisco (2003)Google Scholar
  15. 15.
    Yu, L., Liu, H.: Feature selection for high-dimensional data: A fast correlation-based filter solution. In: 20th Int. Conf. on Machine Learning, pp. 856–863 (2003)Google Scholar
  16. 16.
    Zelezny, F., Lavrac, N.: Propositionalization-Based Relational Subgroup Discovery with RSD. Machine Learning 62(1-2), 33–63 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Carlos Abreu Ferreira
    • 1
  • João Gama
    • 2
  • Vítor Santos Costa
    • 3
  1. 1.LIAAD-INESC and ISEPPolytechnic Institute of PortoPortoPortugal
  2. 2.LIAAD-INESC and Faculty of EconomicsUniversity of PortoPortoPortugal
  3. 3.CRACS-INESC and Faculty of SciencesUniversity of PortoPortoPortugal

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