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AniMoveMineR: Animal Behavior Exploratory Analysis Using Association Rules Mining
Abstract:Environmental changes and major natural disasters are most prevalent in the world due to the damage that humanity has caused to nature and these damages directly affect the lives of animals. Thus, the study of animal behavior and their interactions with the environment can provide knowledge that guides researchers and public agencies in preservation and conservation actions. Exploratory analysis of animal movement can determine the patterns of animal behavior and with technological advances the ability of animals to be tracked and, consequently, behavioral studies have been expanded. There is a lot of research on animal movement and behavior, but we note that a proposal that combines resources and allows for exploratory analysis of animal movement and provide statistical measures on individual animal behavior and its interaction with the environment is missing. The contribution of this paper is to present the framework AniMoveMineR, a unified solution that aggregates trajectory analysis and data mining techniques to explore animal movement data and provide a first step in responding questions about the animal individual behavior and their interactions with other animals over time and space. We evaluated the framework through the use of monitored jaguar data in the city of Miranda Pantanal, Brazil, in order to verify if the use of AniMoveMineR allows to identify the interaction level between these jaguars. The results were positive and provided indications about the individual behavior of jaguars and about which jaguars have the highest or lowest correlation. Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 543
 N. Phan, Mining Object Movement Patterns from Trajectory Data, phdthesis, Universit Monpellier 2, France, 2013.
 A. Dekhtyar Lecture Notes on Data Science - DATA 301., California Polytechnic State University. 2016. Available from: http://users.csc.calpoly.edu/ dekhtyar/DATA301-Spring2016/lectures/lec03.301.pdf.
 M. Wikelski,R. Kays Movebank: archive, analysis and sharing of animal movement data. Hosted by the Max Planck Institute for Ornithology.Available from: www.movebank.org. Accessed in: 10/01/2016 (2016).
 S. Dodge et al., The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data, Movement Ecology, vol. 1, p. 3, 2013, doi: 10.1186/2051-3933-1-3.
 H. Edelhoff, J. Signer, and N. Balkenhol, Path segmentation for beginners: an overview of current methods for detecting changes in animal movement patterns, Movement Ecology, vol. 4, no. 1, p. 21, Dec. 2016, doi: 10.1186/s40462-016-0086-5.
 T. Sippel, J. Holdsworth, T. Dennis, and J. Montgomery, Investigating Behaviour and Population Dynamics of Striped Marlin (Kajikia audax) from the Southwest Pacific Ocean with Satellite Tags, PLOS ONE, vol. 6, no. 6, p. e21087, Jun. 2011, doi: 10.1371/journal.pone.0021087.
 J. Zhao, C. Tian, F. Zhang, C. Xu, and S. Feng, Understanding temporal and spatial travel patterns of individual passengers by mining smart card data, in 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), 2014, pp. 29912997, doi: 10.1109/ITSC.2014.6958170.
 E. Gurarie, C. Bracis, M. Delgado, T. D. Meckley, I. Kojola, and C. M. Wagner, What is the animal doing? Tools for exploring behavioural structure in animal movements, J Anim Ecol, vol. 85, no. 1, pp. 6984, Jan. 2016, doi: 10.1111/1365-2656.12379.
 M. Lavielle, Using Penalized Contrasts for the Change-point Problem, Signal Process., vol. 85, no. 8, pp. 15011510, Aug. 2005, doi: 10.1016/j.sigpro.2005.01.012.
 L. G. Torres, R. A. Orben, I. Tolkova, and D. R. Thompson, Classification of Animal Movement Behavior through Residence in Space and Time, PLOS ONE, vol. 12, no. 1, p. e0168513, Jan. 2017, doi: 10.1371/journal.pone.0168513.
 W. H. Burt, Territoriality and Home Range Concepts as Applied to Mammals, J Mammal, vol. 24, no. 3, pp. 346352, Aug. 1943, doi: 10.2307/1374834.
 C. Calenge, The package adehabitat for the R software: A tool for the analysis of space and habitat use by animals, Ecological Modelling, vol. 197, no. 3, pp. 516519, Aug. 2006, doi: 10.1016/j.ecolmodel.2006.03.017.
 D. D. Mari and S. Kotz, Correlation and Dependence. London: Imperial College Press, 2001. ISBN: 978-1-86094-264-8.
 T. Cheng and J. Wang, Integrated Spatio-temporal Data Mining for Forest Fire Prediction, Transactions in GIS, vol. 12, no. 5, pp. 591611, 2008, doi: 10.1111/j.1467-9671.2008.01117.x.
 G. M. Jacob and S. M. Idicula, Detection of flock movement in spatio-temporal database using clustering techniques - An experience, in 2012 International Conference on Data Science Engineering (ICDSE), 2012, pp. 6974, doi: 10.1109/ICDSE.2012.6282312.
 S. Brin, R. Motwani, J. D. Ullman, and S. Tsur, Dynamic Itemset Counting and Implication Rules for Market Basket Data, in Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, New York, NY, USA, 1997, pp. 255264, doi: 10.1145/253260.253325.
 R. L. Plackett, Karl Pearson and the Chi-Squared Test, International Statistical Review / Revue Internationale de Statistique, vol. 51, no. 1, pp. 5972, 1983, doi: 10.2307/1402731.
 S. A. Alvarez, Chi-squared computation for association rules: Preliminary results, 2003.
 C. Calenge and contributions from S. D. and M. Royer,adehabitatLT: Analysis of Animal Movements. France, 2015.Available from: https://cran.r-project.org/web/packages/adehabitatLT/index.html
 B. J. Worton, Kernel Methods for Estimating the Utilization Distribution in Home-Range Studies, Ecology, vol. 70, no. 1, pp. 164168, 1989, doi: 10.2307/1938423.
 T. Vincenty, Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations., Survey Review, vol. 23, no. 176, pp. 8893, Apr. 1975, doi: 10.1179/sre.19126.96.36.199.
 M. Hahsler, B. Gruen, K. Hornik,arules - A Computational Environment for Mining Association Rules and Frequent Item Sets.Journal of Statistical Software, doi: 10.18637/jss.v014.i15.
 A. C. Acock and G. R. Stavig, A Measure of Association for Nonparametric Statistics, Social Forces, vol. 57, no. 4, pp. 13811386, 1979, doi: 10.2307/2577276.
 R. G. Morato et al., Jaguar movement database: a GPS-based movement dataset of an apex predator in the Neotropics, Ecology, vol. 99, no. 7, pp. 16911691, 2018, doi: 10.1002/ecy.2379.
 J. Manimaran and T. Velmurugan, Analysing the Quality of Association Rules by Computing an Interestingness Measures, Indian Journal of Science and Technology, vol. 8, no. 15, Jul. 2015, doi: 10.17485/ijst/2015/v8i15/76693.