Poster 148: Unsupervised Clustering of Golden Eagle Telemetry Data

Authors: Natalya I. Rapstine (US Geological Survey), Jeff A. Tracey (US Geological Survey), Janice M. Gordon (US Geological Survey), Robert N. Fisher (US Geological Survey)

Abstract: We 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.

Best Poster Finalist (BP): no

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