Abstract
The increasing availability of animal tracking data brings us opportunities and challenges to intuitively understand the mechanisms of animal activities. In this paper, we aim to discover animal movement patterns from animal trajectory data. In particular, we propose a notion of continuous activity pattern as the concise representation of underlying similar spatio-temporal movements, and develop an extension and refinement framework to discover the patterns. We first preprocess the trajectories into significant semantic locations with time property. Then, we apply a projection-based approach to generate candidate patterns and refine them to generate true patterns. A sequence graph structure and a simple and effective processing strategy is further developed to reduce the computational overhead. The proposed approaches are extensively validated on both real GPS datasets and large synthetic datasets.
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Wang, Y., Luo, Z., Yan, B., Takekawa, J., Prosser, D., Newman, S. (2014). Mining Continuous Activity Patterns from Animal Trajectory Data. In: Luo, X., Yu, J.X., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2014. Lecture Notes in Computer Science(), vol 8933. Springer, Cham. https://doi.org/10.1007/978-3-319-14717-8_19
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DOI: https://doi.org/10.1007/978-3-319-14717-8_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14716-1
Online ISBN: 978-3-319-14717-8
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