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

DOI: 10.1007/978-3-642-22922-0_25

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6849)
Cite this paper as:
Bernecker T. et al. (2011) Quality of Similarity Rankings in Time Series. In: Pfoser D. et al. (eds) Advances in Spatial and Temporal Databases. SSTD 2011. Lecture Notes in Computer Science, vol 6849. Springer, Berlin, Heidelberg

Abstract

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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