Publikation: Spatio-Temporal Clustering Benchmark for Collective Animal Behavior
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Various spatio-temporal clustering methods have been proposed to detect groups of jointly moving objects in space and time. However, such spatio-temporal clustering methods are rarely compared against each other to evaluate their performance in discovering moving clusters. Hence, in this work, we present a spatio-temporal clustering benchmark for the field of collective animal behavior. Our reproducible benchmark proposes synthetic datasets with ground truth and scalable implementations of spatio-temporal clustering methods. The benchmark reveals that temporal extensions of standard clustering algorithms are inherently useful for the scalable detection of moving clusters in collective animal behavior.
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CAKMAK, Eren, Manuel PLANK, Daniel S. CALOVI, Alex JORDAN, Daniel A. KEIM, 2021. Spatio-Temporal Clustering Benchmark for Collective Animal Behavior. 1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility (HANIMOB’21). Beijing, China, 2. Nov. 2021. In: 1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility (HANIMOB’21),. New York, NY: ACM, 2021. Available under: doi: 10.1145/3486637.3489487BibTex
@inproceedings{Cakmak2021Spati-55277, year={2021}, doi={10.1145/3486637.3489487}, title={Spatio-Temporal Clustering Benchmark for Collective Animal Behavior}, publisher={ACM}, address={New York, NY}, booktitle={1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility (HANIMOB’21),}, author={Cakmak, Eren and Plank, Manuel and Calovi, Daniel S. and Jordan, Alex and Keim, Daniel A.} }
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