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Spatio-Temporal Clustering Benchmark for Collective Animal Behavior
- 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
- Subjects :
- Ground truth
Computer science
computer.software_genre
Field (computer science)
Trajectory clustering
ComputingMethodologies_PATTERNRECOGNITION
Scalability
Benchmark (computing)
Data mining
Collective animal behavior
Spatio-Temporal Clustering, Trajectory Clustering, Benchmark, Moving Clusters, Collective Animal Behavior
ddc:004
Cluster analysis
Spatio temporal clustering
computer
Subjects
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