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
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5644)

Abstract

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 Lp-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.

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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

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