Presentation
Poster 148: Unsupervised Clustering of Golden Eagle Telemetry Data
SessionResearch Posters Display
Event Type
Posters
Research Posters
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EX
EXH
TimeThursday, 21 November 20198:30am - 5pm
LocationE Concourse
DescriptionWe use a recurrent autoencoder neural network to encode sequential California golden eagle telemetry data. The encoding is followed by an unsupervised clustering technique, Deep Embedded Clustering (DEC), to iteratively cluster the data into a chosen number of behavior classes. We apply the method to simulated movement data sets and telemetry data for a Golden Eagle. The DEC achieves better unsupervised clustering accuracy scores for the simulated data sets as compared to the baseline K-means clustering result.
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