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

@inproceedings{Achtert2009ELKIIT,
  title={ELKI in Time: ELKI 0.2 for the Performance Evaluation of Distance Measures for Time Series},
  author={Elke Achtert and Thomas Bernecker and Hans-Peter Kriegel and Erich Schubert and Arthur Zimek},
  booktitle={SSTD},
  year={2009}
}
ELKI is a uni ed software framework, designed as a tool suitable for evaluation of di erent algorithms on high dimensional realvalued 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-nearestneighbor… CONTINUE READING
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OpenSubspace: an open source framework for evaluation and exploration of subspace clustering algorithms in WEKA

  • E. Müller, I. Assent, S. Günnemann, T. Jansen, T. Seidl
  • Proc. OSDM@PAKDD.
  • 2009
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