Abstract
Detection of spatial outliers is a spatial data mining task aimed at discovering data observations that differ from other data observations within its spatial neighborhood. Some considerations that depend on the problem domain and data characteristics have to be taken into account for the selection of the data mining algorithms to be used in each data mining project. This massive amount of possible algorithm combinations makes it necessary to design a knowledge discovery process for detection of local spatial outliers in order to perform this activity in a standardized way. This work provides a proposal for this knowledge discovery process based on the Knowledge Discovery in Database process (KDD) and a proof of concept of this design using real world data.
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Acknowledgments
The research presented in this paper was partially funded by the PhD Scholarship Program to reinforce R&D&I areas (2016-2020) of the Universidad Tecnológica Nacional, 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, and the anonymous reviewers of this work for their valuable comments and suggestions.
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Rottoli, G.D., Merlino, H., García-Martínez, R. (2018). Knowledge Discovery Process for Detection of Spatial Outliers. In: Mouhoub, M., Sadaoui, S., Ait Mohamed, O., Ali, M. (eds) Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science(), vol 10868. Springer, Cham. https://doi.org/10.1007/978-3-319-92058-0_6
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