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