Clustering for Automated Exploratory Pattern Discovery in Animal Behavioral Data.

ABSTRACT: Traditional methods of data analysis in animal behavior research are usually based on measuring behavior by manually coding a set of chosen behavioral parameters, which is naturally prone to human bias and error, and is also a tedious labor-intensive task. Machine learning techniques are increasingly applied to support researchers in this field, mostly in a supervised manner: for tracking animals, detecting land marks or recognizing actions. Unsupervised methods are increasingly used, but are under-explored in the context of behavior studies and applied contexts such as behavioral testing of dogs. This study explores the potential of unsupervised approaches such as clustering for the automated discovery of patterns in data which have potential behavioral meaning. We aim to demonstrate that

SUBMITTER: Menaker T 

PROVIDER: S-EPMC9260587 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature