Co-location rules discovery process focused on reference spatial features using decision tree learning

2018-11-302018-11-302017Advances in Artificial Intelligence: From Theory to Practice 10350: 221-226 (2017)http://hdl.handle.net/20.500.12272/3308The 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.application/pdfenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/Co-location patternsSpatial data miningDecision trees algorithmsMaximal cliquesKnowledge discovery processCo-location rules discovery process focused on reference spatial features using decision tree learninginfo:eu-repo/semantics/articleRottoli, Giovanni Daián ; Merlino, Hernán Daniel ; García Martínez, RamónNo comercial con fines académicosAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttps://doi.org/10.1007/978-3-319-60042-0_25