ELKI in Time: ELKI 0.2 for the Performance Evaluation of Distance Measures for Time Series

  • Elke Achtert
  • Thomas Bernecker
  • Hans-Peter Kriegel
  • Erich Schubert
  • Arthur Zimek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5644)


ELKI is a unified software framework, designed as a tool suitable for evaluation of different algorithms on high dimensional real-valued feature-vectors. A special case of high dimensional real-valued feature-vectors are time series data where traditional distance measures like L p -distances can be applied. However, also a broad range of specialized distance measures like, e.g., dynamic time-warping, or generalized distance measures like second order distances, e.g., shared-nearest-neighbor distances, have been proposed. The new version ELKI 0.2 now is extended to time series data and offers a selection of these distance measures. It can serve as a visualization- and evaluation-tool for the behavior of different distance measures on time series data.


Distance Measure Time Series Data Index Structure Edit Distance Dynamic Time Warping 
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.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  2. 2.
    Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid prototyping for complex data mining tasks. In: Proc. KDD (2006)Google Scholar
  3. 3.
    Müller, E., Assent, I., Günnemann, S., Jansen, T., Seidl, T.: OpenSubspace: an open source framework for evaluation and exploration of subspace clustering algorithms in WEKA. In: Proc. OSDM@PAKDD (2009)Google Scholar
  4. 4.
    Hellerstein, J.M., Naughton, J.F., Pfeffer, A.: Generalized search trees for database systems. In: Proc. VLDB (1995)Google Scholar
  5. 5.
    Aßfalg, J., Kriegel, H.P., Kröger, P., Kunath, P., Pryakhin, A., Renz, M.: T-Time: threshold-baed data mining on time series. In: Proc. ICDE (2008)Google Scholar
  6. 6.
    Achtert, E., Kriegel, H.P., Zimek, A.: ELKI: a software system for evaluation of subspace clustering algorithms. In: Ludäscher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol. 5069, pp. 580–585. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Achtert, E., Böhm, C., Kriegel, H.P., Kröger, P., Zimek, A.: Robust, complete, and efficient correlation clustering. In: Proc. SDM (2007)Google Scholar
  8. 8.
    Achtert, E., Böhm, C., Kriegel, H.P., Kröger, P., Zimek, A.: On exploring complex relationships of correlation clusters. In: Proc. SSDBM (2007)Google Scholar
  9. 9.
    Achtert, E., Böhm, C., Kriegel, H.P., Kröger, P., Müller-Gorman, I., Zimek, A.: Detection and visualization of subspace cluster hierarchies. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 152–163. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Berndt, D., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop (1994)Google Scholar
  11. 11.
    Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: Proc. ICDE (2002)Google Scholar
  12. 12.
    Chen, L., Özsu, M., Oria, V.: Robust and fast similarity search for moving object trajectories. In: Proc. SIGMOD (2005)Google Scholar
  13. 13.
    Chen, L., Ng, R.: On the marriage of Lp-norms and edit distance. In: Proc. VLDB (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Elke Achtert
    • 1
  • Thomas Bernecker
    • 1
  • Hans-Peter Kriegel
    • 1
  • Erich Schubert
    • 1
  • Arthur Zimek
    • 1
  1. 1.Ludwig-Maximilians-Universität MünchenMünchenGermany

Personalised recommendations