Knowledge discovery process for detection of spatial outliers
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 o...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | Argentina |
| Institución: | Universidad Tecnológica Nacional |
| Repositorio: | Repositorio Institucional Abierto (UTN) |
| Idioma: | inglés |
| OAI Identifier: | oai:ria.utn.edu.ar:20.500.12272/3309 |
| Acceso en línea: | http://hdl.handle.net/20.500.12272/3309 https://doi.org/10.1007/978-3-319-92058-0_6 |
| Access Level: | acceso abierto |
| Palabra clave: | Spatial outliers Local outliers Spatial data mining Knowledge discovery process Spatial clustering |
| Sumario: | 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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