Skip to main content
SpringerLink
Log in

(TD)2PaM: A Constraint-Based Algorithm for Mining Temporal Patterns in Transactional Databases

  • Conference paper
Progress in Artificial Intelligence (EPIA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8154))

Included in the following conference series:

Abstract

The analysis of frequent behaviors regarding temporal issues begins to achieve some interest, in particular in the area of health care. However, existing approaches tend to ignore the temporal information and only make use of the order among events occurrence. In this paper, we introduce the notion of temporal constraint, and propose three instantiations of it: complete cyclic temporal constraints, partial cyclic temporal constraints and timespan constraints. Additionally, we propose a new algorithm – (TD)2PaM, that together with these constraints, makes possible to focus the pattern mining process on looking for cyclic and timespan patterns. Experimental results reveal the algorithm to be as efficient as its predecessors, and able to discover more informed patterns.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Mitsa, T.: Temporal Data Mining. Chapman & Hall/CRC (2010)

    Google Scholar 

  2. Allen, J.: Maintaining knowledge about temporal intervals. Communications of the ACM 26(11), 832–843 (1983)

    Article  MATH  Google Scholar 

  3. Zhou, Q., Fikes, R.: A Reusable Time Ontology. In: Press, A. (ed.) AAAI Workshop on Ontologies for the Semantic Web (2002)

    Google Scholar 

  4. 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 

  5. Chen, Y., Chiang, M., Ko, M.: Discovering time-interval sequential patterns in sequence databases. Expert Systems Applications 25, 343–354 (2003)

    Article  Google Scholar 

  6. Mannila, H., Toivonen, H., Verkamo, A.: Discovery of Frequent Episodes in Event Sequences. Data Mining and Knowledge Discovery 1(3), 259–289 (1997)

    Article  Google Scholar 

  7. Lee, C., Chen, M., Lin, C.: Progressive partition miner: an efficient algorithm for mining general temporal association rules. IEEE Transaction on Knowledge and Data Engineering 15(4), 1004–1017 (2003)

    Article  Google Scholar 

  8. Huang, J., Dai, B., Chen, M.: Twain: Two-End Association Miner with Precise Frequent Exhibition Periods. ACM Transactions on Knowledge Discovery from Data 1(2) (2007)

    Google Scholar 

  9. Ozden, B., Ramaswamy, S., Silberschtaz, A.: Cyclic Association Rules. In: International Conference on Data Engineering, pp. 412–421 (1998)

    Google Scholar 

  10. Laxman, S., Sastry, P.S.: A survey of temporal data mining. Sadhana 31(2), 173–198 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  11. Han, J., Dong, G., Yin, Y.: Efficient mining of partial periodic patterns in time series database. In: International Conference on Data Engineering, Sydney, Australia, pp. 106–115 (1999)

    Google Scholar 

  12. Ramaswamy, S., Mahajan, S., Silberschatz, A.: On the Discovery of Interesting Patterns in Association Rules. In: International Conference on Very Large Databases, New York, USA, pp. 368–379 (1998)

    Google Scholar 

  13. Li, Y., Ning, P., Wang, X., Jajodia, S.: Discovering calendar-based temporal association rules. Data Knowledge Engineering 44, 193–218 (2003)

    Article  Google Scholar 

  14. Zimbrão, G., Souza, J., Almeida, V.T., Silva, W.: An Algorithm to Discover Calendar-based Temporal Association. In: Workshop on Temporal Data Mining at ACM SIGKDD Int’l Conf on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada (2002)

    Google Scholar 

  15. Antunes, C., Bebiano, T.: Mining Patterns with Domain Knowledge: a case study on multi-language data. In: International Conference on Information Systems, Shanghai, China, pp. 167–172 (2012)

    Google Scholar 

  16. Srikant, R., Agrawal, R.: Mining Generalized Association Rules. In: International Conference on Very Large Databases, Zurich, pp. 407–419 (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dep. Computer Science and Engineering, Instituto Superior Técnico, Technical University of Lisbon, Av Rovisco Pais, 1049-001, Lisboa, Portugal

    Sílvia Moura Pina & Cláudia Antunes

Authors
  1. Sílvia Moura Pina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cláudia Antunes
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Informatics Department, University of Lisbon, Campo Grande, 174-016, Lisbon, Portugal

    Luís Correia

  2. Information Systems Department, University of Minho, Campus de Azurém, 4800-058, Guimarães, Portugal

    Luís Paulo Reis

  3. Department of Education, University of the Azores, Campus de Angra do Heroísmo, Angra do Heroísma, 9700-042, Azores, Portugal

    José Cascalho

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pina, S.M., Antunes, C. (2013). (TD)2PaM: A Constraint-Based Algorithm for Mining Temporal Patterns in Transactional Databases. In: Correia, L., Reis, L.P., Cascalho, J. (eds) Progress in Artificial Intelligence. EPIA 2013. Lecture Notes in Computer Science(), vol 8154. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40669-0_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40669-0_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40668-3

  • Online ISBN: 978-3-642-40669-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation