Abstract
The co-location discovery process serves to find subsets of spatial features frequently located together. Many algorithms and methods have been designed in recent years; however, finding this kind of patterns around specific spatial features is a task in which the existing solutions provide incorrect results. Throughout this paper we propose a knowledge discovery process to find co-location patterns focused on reference features using decision tree learning algorithms on transactional data generated using maximal cliques. A validation test of this process is provided.
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Acknowledgements
The research presented in this paper was partially funded by the PhD Scholarship Program to reinforce R+D+I areas (2016–2020) of the Technological National University, Research Project 80020160400001LA of National University of Lanús, and PIO CONICET-UNLa 22420160100032CO of National Research Council of Science and Technology (CONICET), Argentina. The authors also want to extend their gratitude to Kevin-Mark Bozell Poudereux for proofreading the translation.
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Rottoli, G.D., Merlino, H., García-Martinez, R. (2017). Co-location Rules Discovery Process Focused on Reference Spatial Features Using Decision Tree Learning. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_25
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