Expectation-Maximization Binary Clustering for Behavioural Annotation

• Published in: PloS one 2016-03, Vol.11 (3), p.e0151984-e0151984
• Main Authors: Garriga, Joan, Palmer, John R B, Oltra, Aitana, Bartumeus, Frederic
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Animals; Annotations; Behavior, Animal - physiology; Biology and Life Sciences; Cluster Analysis; Clustering; Computational biology; Computer applications; Computer science; Computer simulation; Data loss; Datasets; Dermochelys coriacea; Ecology; Empirical analysis; Evolutionary adaptation; Introduced species; Iterative algorithms; Iterative methods; Laboratories; Markov chains; Maximization; Maximum likelihood estimation; Methods; Models, Statistical; Models, Theoretical; Multivariate analysis; Parameter estimation; Physical Sciences; Probability; Research and Analysis Methods; Segmentation; Semantics; Social Sciences; Trajectories (Physics); Trajectory analysis; Variables
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0151984

The growing capacity to process and store animal tracks has spurred the development of new methods to segment animal trajectories into elementary units of movement. Key challenges for movement trajectory segmentation are to (i) minimize the need of supervision, (ii) reduce computational costs, (iii) minimize the need of prior assumptions (e.g. simple parametrizations), and (iv) capture biologically meaningful semantics, useful across a broad range of species. We introduce the Expectation-Maximization binary Clustering (EMbC), a general purpose, unsupervised approach to multivariate data clustering. The EMbC is a variant of the Expectation-Maximization Clustering (EMC), a clustering algorithm based on the maximum likelihood estimation of a Gaussian mixture model. This is an iterative algorithm with a closed form step solution and hence a reasonable computational cost. The method looks for a good compromise between statistical soundness and ease and generality of use (by minimizing prior assumptions and favouring the semantic interpretation of the final clustering). Here we focus on the suitability of the EMbC algorithm for behavioural annotation of movement data. We show and discuss the EMbC outputs in both simulated trajectories and empirical movement trajectories including different species and different tracking methodologies. We use synthetic trajectories to assess the performance of EMbC compared to classic EMC and Hidden Markov Models. Empirical trajectories allow us to explore the robustness of the EMbC to data loss and data inaccuracies, and assess the relationship between EMbC output and expert label assignments. Additionally, we suggest a smoothing procedure to account for temporal correlations among labels, and a proper visualization of the output for movement trajectories. Our algorithm is available as an R-package with a set of complementary functions to ease the analysis.