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 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.
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Achtert, E., Bernecker, T., Kriegel, HP., Schubert, E., Zimek, A. (2009). ELKI in Time: ELKI 0.2 for the Performance Evaluation of Distance Measures for Time Series. In: Mamoulis, N., Seidl, T., Pedersen, T.B., Torp, K., Assent, I. (eds) Advances in Spatial and Temporal Databases. SSTD 2009. Lecture Notes in Computer Science, vol 5644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02982-0_35
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DOI: https://doi.org/10.1007/978-3-642-02982-0_35
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