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.
CITATION STYLE
Rottoli, G. D., Merlino, H., & García-Martinez, R. (2017). Co-location rules discovery process focused on reference spatial features using decision tree learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10350 LNCS, pp. 221–226). Springer Verlag. https://doi.org/10.1007/978-3-319-60042-0_25
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