Modeling and Clustering the Behavior of Animals Using Hidden Markov Models

0462894 - ÚI 2017 RIV DE eng C - Conference Paper (international conference)
Šabata, T. - Borovička, T. - Holeňa, Martin
Modeling and Clustering the Behavior of Animals Using Hidden Markov Models.
Proceedings ITAT 2016: Information Technologies - Applications and Theory. Aachen & Charleston: Technical University & CreateSpace Independent Publishing Platform, 2016 - (Brejová, B.), s. 172-178. CEUR Workshop Proceedings, V-1649. ISBN 978-1-5370-1674-0. ISSN 1613-0073.
[ITAT 2016. Conference on Theory and Practice of Information Technologies /16./. Tatranské Matliare (SK), 15.09.2016-19.09.2016]
Institutional support: RVO:67985807
Keywords : behavior patterns * behavioral sequences * clustering * hidden Markov models * Kullback-Leibler divergence
Subject RIV: IN - Informatics, Computer Science
http://ceur-ws.org/Vol-1649/172.pdf

The objectives of this article are to model behavior of individual animals and to cluster the resulting models in order to group animals with similar behavior patterns. Hidden Markov models are considered suitable for clustering purposes. Their clustering is well studied, however, only if the observable variables can be assumed to be Gaussian mixtures, which is not valid in our case. Therefore, we use the Kullback-Leibler divergence to cluster hidden Markov models with observable variables that have an arbitrary distribution. Hierarchical and spectral clustering is applied. To evaluate the modeling approach, an experiment was performed and an accuracy of 83.86% was reached in predicting behavioral sequences of individual animals. Results of clustering were evaluated by means of statistical descriptors of the animals and by a domain expert, both methods confirm that the results of clustering are meaningful.
Permanent Link: http://hdl.handle.net/11104/0262246