Learning Pattern Graphs for Multivariate Temporal Pattern Retrieval

  • Sebastian Peter
  • Frank Höppner
  • Michael R. Berthold
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)


We propose a two-phased approach to learn pattern graphs, a powerful pattern language for complex, multivariate temporal data, which is capable of reflecting more aspects of temporal patterns than earlier proposals. The first phase aims at increasing the understandability of the graph by finding common substructures, thereby helping the second phase to specialize the graph learned so far to discriminate against undesired situations. The usefulness is shown on data from the automobile industry and the libras data set by taking the accuracy and the knowledge gain of the learned graphs into account.


Wireless Sensor Network Temporal Constraint Outgoing Edge Target Class Driving Cycle 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Basile, T.M.A., Di Mauro, N., Ferilli, S., Esposito, F.: Relational Temporal Data Mining for Wireless Sensor Networks. In: Serra, R., Cucchiara, R. (eds.) AI*IA 2009. LNCS(LNAI), vol. 5883, pp. 416–425. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Batal, I., Valizadegan, H., Cooper, G.F., Hauskrecht, M.: A pattern mining approach for classifying multivariate temporal data. In: Proc. IEEE Int. Conf. Bioinformatics BioMed, pp. 358–365 (2011)Google Scholar
  3. 3.
    Berlingerio, M., Pinelli, F., Nanni, M., Giannotti, F.: Temporal mining for interactive workflow data analysis. In: Proc. 15th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, KDD 2009, pp. 109–118 (2009)Google Scholar
  4. 4.
    Frank, A., Asuncion, A.: UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences (2010)Google Scholar
  5. 5.
    Höppner, F.: Discovery of temporal patterns – learning rules about the qualitative behaviour of time series. In: Proc. of the 5th Europ. Conf. on Principles of Data Mining and Knowl. Discovery, pp. 192–203. Springer (2001)Google Scholar
  6. 6.
    Höppner, F., Peter, S., Berthold, M.R.: Enriching Multivariate Temporal Patterns with Context Information to Support Classification. In: Moewes, C., Nürnberger, A. (eds.) Computational Intelligence in Intelligent Data Analysis. SCI, vol. 445, pp. 195–206. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Mörchen, F.: Unsupervised pattern mining from symbolic temporal data. ACM SIGKDD Explorations Newsletter 9(1), 41–55 (2007)CrossRefGoogle Scholar
  8. 8.
    Peter, S., Höppner, F., Berthold, M.R.: Pattern graphs: A knowledge-based tool for multivariate temporal pattern retrieval. In: Proc. IEEE Conf. Intelligent Systems. IEEE (2012)Google Scholar
  9. 9.
    Wang, J., Han, J.: Bide: Efficient mining of frequent closed sequences. In: Int. Conf on Data Engineering, pp. 79–90 (2004)Google Scholar
  10. 10.
    Wang, L., Jiang, T.: On the complexity of multiple sequence alignment. Journal of Computational Biology 1(4), 337–348 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sebastian Peter
    • 1
  • Frank Höppner
    • 2
  • Michael R. Berthold
    • 1
  1. 1.Nycomed-Chair for Bioinformatics and Information Mining, Dept. of Computer ScienceUniversity of KonstanzKonstanzGermany
  2. 2.Dept. of Computer ScienceOstfalia University of Applied SciencesWolfenbüttelGermany

Personalised recommendations