ELKI in Time: ELKI 0.2 for the Performance Evaluation of Distance Measures for Time Series
نویسندگان
چکیده
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 distances, have been proposed. The new version ELKI 0.2 now is extended to time series data and o ers a selection of these distance measures. It can serve as a visualizationand evaluation-tool for the behavior of di erent distance measures on time series data.
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