Association rules mining applied in the animal movement exploratory analysis
The animal movement analysis determines the animal behavior, which is the basis for understanding the interaction between species and the environment and to guide actions of preservation and conservation. The challenge is how to explore this movement data, getting indications about how the animal behaves over time and space. In this sense, a framework to animal movement exploratory analysis is presented, that combines algorithms for spatiotemporal data analysis and association rules mining, as a first step to answer questions related to animal behavior. We performed the framework’s evaluation in the exploratory analysis of monitored monkeys (Cebus capucinus) in the Panamá.
basket data. In Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data. ACM,
New York, NY, USA, pp. 255–264, 1997.
Burt, W. H. Territoriality and home range concepts as applied to mammals. Journal of Mammalogy 24 (3): 346–352,1943.
Calenge, C. The package adehabitat for the R software: A tool for the analysis of space and habitat use by animals. Ecological Modelling 197 (3): 516–519, 2006.
Calenge, C. and Royer, c. f. S. D. a. M. adehabitatLT: Analysis of Animal Movements. https://cran.r-
CENAP. Centro nacional de pesquisa e conservação de mamíferos carnívoros. http://www.icmbio.gov.br/cenap, 2019.
Cheng, T. and Wang, J. Integrated Spatio-temporal Data Mining for Forest Fire Prediction. Transactions in
GIS 12 (5): 591–611, 2008.
Dekhtyar, A. Lecture notes on data science - data 301. http://users.csc.calpoly.edu/ dekhtyar/DATA301-
Demsar, U., Buchin, K., Cagnacci, F., Safi, K., Speckmann, B., Van de Weghe, N., Weiskopf, D., and Weibel,
R. Analysis and visualisation of movement: an interdisciplinary review. Movement Ecology 3 (1): 5, 2015.
Edelhoff, H., Signer, J., and Balkenhol, N. Path segmentation for beginners: an overview of current methods for
detecting changes in animal movement patterns. Movement Ecology vol. 4, pp. 21, 2016.
Gurarie, E., Andrews, R. D., and Laidre, K. L. A novel method for identifying behavioural changes in animal
movement data. Ecology Letters 12 (5): 395–408, 2009.
Hahsler, M., Gruen, B., and Hornik, K. arules A Computational Environment for Mining Association Rules and
Frequent Item Sets. Journal of Statistical Software 14 (15): 1–25, 2005.
Jacob, G. and Idicula, S. Detection of flock movement in spatio-temporal database using clustering techniques - An experience. In 2012 International Conference on Data Science Engineering (ICDSE). IEEE Xplore, Kochi, India,
pp. 69–74, 2012.
Kernohan, B. J., Gitzen, R. A., and Millspaugh, J. J. Chapter 5 - Analysis of Animal Space Use and Movements.
In J. J. Millspaugh and J. M. Marzluff (Eds.), Analysis of Animal Space Use and Movements. Academic Press, San
Diego, pp. 125–166, 1978.
Komsta, L. outliers: Tests for outliers. https://CRAN.R-project.org/package=outliers, 2011.
Li, Z., Ding, B., Wu, F., Lei, T. K. H., Kays, R., and Crofoot, M. C. Attraction and Avoidance Detection from
Movements. Proc. VLDB Endow. 7 (3): 157–168, 2013.
Manimaran, J. and Velmurugan, T. Analysing the Quality of Association Rules by Computing an Interestingness
Measures. Indian Journal of Science and Technology 8 (15): 1–12, 2015.
Mari, D. and Kotz, S. Correlation and Dependence. Imperial College Press, London, 2001.
Phan, N. phdthesis. Ph.D. thesis, Université Monpellier 2, France, 2013.
Planck, M. Instituto max planck de ornitologia. https:// www.orn.mpg.de/en, 2019.
Snowdon, C. T. O significado da pesquisa em Comportamento Animal. Estudos de Psicologia (Natal) 4 (2): 365–373, 1999.
Taylor, R. Interpretation of the Correlation Coefficient: A Basic Review. Journal of Diagnostic Medical Sonogra-
phy 6 (1): 35–39, 1990.
Torres, L. G., Orben, R. A., Tolkova, I., and Thompson, D. R. Classification of Animal Movement Behavior
through Residence in Space and Time. PLOS ONE 12 (1): e0168513, 2017.
Vincenty, T. Direct and Inverse Solutions of Geodesics on the Ellipsoid with Application of Nested Equations. Survey Review 23 (176): 88–93, 1975.
Zhang, J., OReilly, K. M., Perry, G. L. W., Taylor, G. A., and Dennis, T. E. Extending the Functionality of
Behavioural Change-Point Analysis with k-Means Clustering: A Case Study with the Little Penguin (Eudyptula
minor). PLoS ONE 10 (4): 1–14, 2015.