Data-Driven Analysis of Animal Behavioral Patterns

Measurement of behavior is a major challenge in many animal-related disciplines, including ACI. This usually requires choosing specific parameters for measuring, related to the investigated hypothesis. Therefore, a key challenge is determining what a priori parameters are informational for a given experiment. The scope of this challenge is raised even further by the emerging computational approaches for animal detection and tracking, as the automation of behavioral measurement makes the possibilities for measuring behavioral parameters practically endless. This research approaches these challenges by proposing a framework for guiding the researchers decision making in their future data analysis. The envisioned framework is data-driven in the sense that it applies data mining techniques for obtaining insights from experimental data for guiding the choice of certain behavioral parameters. Here, we present the envisioned Data-Driven Framework for Behavioral Pattern Analysis framework and its components and discuss it's current topics and challenges.