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%2F68407700%3A21240%2F16%3A00306336" target="_blank" >RIV/68407700:21240/16:00306336 - 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

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

CEUR workshop proceedings

ISSN

1613-0073

e-ISSN

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Svazek periodika

2016

Číslo periodika v rámci svazku

1649

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

7

Strana od-do

172-178

Kód UT WoS článku

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EID výsledku v databázi Scopus

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