Modeling and Clustering the Behavior of Animals Using Hidden Markov Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F16%3A00462894" target="_blank" >RIV/67985807:_____/16:00462894 - isvavai.cz</a>

Výsledek na webu

<a href="http://ceur-ws.org/Vol-1649/172.pdf" target="_blank" >http://ceur-ws.org/Vol-1649/172.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Modeling and Clustering the Behavior of Animals Using Hidden Markov Models

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Modeling and Clustering the Behavior of Animals Using Hidden Markov Models

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings ITAT 2016: Information Technologies - Applications and Theory

ISBN

978-1-5370-1674-0

ISSN

1613-0073

e-ISSN

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Počet stran výsledku

7

Strana od-do

172-178

Název nakladatele

Technical University & CreateSpace Independent Publishing Platform

Místo vydání

Aachen & Charleston

Místo konání akce

Tatranské Matliare

Datum konání akce

15. 9. 2016

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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