Quality of Similarity Rankings in Time Series

  • Thomas Bernecker
  • Michael E. Houle
  • Hans-Peter Kriegel
  • Peer Kröger
  • Matthias Renz
  • Erich Schubert
  • Arthur Zimek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6849)


Time series data objects can be interpreted as high- dimensional vectors, which allows the application of many traditional distance measures as well as more specialized measures. However, many distance functions are known to suffer from poor contrast in high-dimensional settings, putting their usefulness as similarity measures into question. On the other hand, shared-nearest-neighbor distances based on the ranking of data objects induced by some primary distance measure have been known to lead to improved performance in high-dimensional settings. In this paper, we study the performance of shared-neighbor similarity measures in the context of similarity search for time series data objects. Our findings are that the use of shared-neighbor similarity measures generally results in more stable performances than that of their associated primary distance measures.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Thomas Bernecker
    • 1
  • Michael E. Houle
    • 2
  • Hans-Peter Kriegel
    • 1
  • Peer Kröger
    • 1
  • Matthias Renz
    • 1
  • Erich Schubert
    • 1
  • Arthur Zimek
    • 1
  1. 1.Ludwig-Maximilians-Universität MünchenMünchenGermany
  2. 2.National Institute of InformaticsTokyoJapan

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