Automatic analysis of rodents behaviour has received growing attention in recent years as rodents are the reference species for large scale pharmacological and genetic screenings. In this paper we propose a new method to identify prototypical high-level behavioural patterns which go beyond simple atomic actions. The method is embedded in a data mining pipeline thought to support behavioural scientists in exploratory data analysis and hypothesis formulation. A case study is presented where the method is capable of learning high-level behavioural prototypes which help discriminating between two strains of mouse having known differences in their behaviour.

Dirichlet Process Mixtures of Multinomials for Data Mining in Mice Behaviour Analysis

Sona, Diego;
2013

Abstract

Automatic analysis of rodents behaviour has received growing attention in recent years as rodents are the reference species for large scale pharmacological and genetic screenings. In this paper we propose a new method to identify prototypical high-level behavioural patterns which go beyond simple atomic actions. The method is embedded in a data mining pipeline thought to support behavioural scientists in exploratory data analysis and hypothesis formulation. A case study is presented where the method is capable of learning high-level behavioural prototypes which help discriminating between two strains of mouse having known differences in their behaviour.
9781479930227
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/251229
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