Finding Discriminative Animal Behaviors from Sequential Bio-Logging Trajectory Data

Abstract

Recent advancement of bio-logging devices such as GPS sensor enables researchers in ecology to quantitatively measure animal trajectories. These animal trajectory data are often represented in the form of multi-dimensional time-series. In this paper, we develop a method for extracting interesting animal behaviors from these multi-dimensional time-series. To this end, we represent a multi-dimensional time-series as a discrete symbol sequence, and introduce some techniques developed in the context of sequential pattern mining, which has been actively studied in the literature of knowledge discovery and data mining. In animal behavior studies, it is often desired to conduct comparative studies for finding different animal behaviors in different groups, e.g, different behaviors between male and female animals etc. We use a sequential pattern mining method designed for finding so-called discriminative sequential patterns, i.e., sequential patterns that are useful for discriminating different group of animals. We apply the method to several animal trajectory datasets for demonstrating its effectiveness.