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Spatio-Temporal Clustering Benchmark for Collective Animal Behavior

Authors :
Manuel Plank
Daniel S. Calovi
Daniel A. Keim
Eren Cakmak
Alex Jordan
Source :
HANIMOB@SIGSPATIAL, 1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility (HANIMOB’21)
Publication Year :
2021

Abstract

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. published

Details

Language :
English
Database :
OpenAIRE
Journal :
HANIMOB@SIGSPATIAL, 1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility (HANIMOB’21)
Accession number :
edsair.doi.dedup.....01b8226c189f7106ae766d209beee58d