Knowledge discovery process for description of spatially referenced clusters

dc.creatorRóttoli, Giovanni
dc.creatorMerlino, Hernán
dc.creatorGarcía Martínez, Ramón
dc.date2017-07
dc.date2017
dc.date2019-12-17T14:27:30Z
dc.date.accessioned2023-07-14T17:46:07Z
dc.date.available2023-07-14T17:46:07Z
dc.identifierhttp://sedici.unlp.edu.ar/handle/10915/87556
dc.identifierissn:2325-9000
dc.identifierisbn:1891706411
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7428766
dc.descriptionSpatial clustering is an important field of spatial data mining and knowledge discovery that serves to partition a spatial data set to obtain disjoint subsets with spatial elements that are similar to each other. Existing algorithms can be used to perform three types of cluster analyses, including clustering of spatial points, regionalization and point pattern analysis. However, all these existing methods do not provide a description of the discovered spatial clusters, which is useful for decision making in many different fields. This work proposes a knowledge discovery process for the description of spatially referenced clusters that uses decision tree learning algorithms. Two proofs of concept of the proposed process using different spatial clustering algorithm on real data are also provided.
dc.descriptionFacultad de Informática
dc.formatapplication/pdf
dc.format410-415
dc.languageen
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.subjectCiencias Informáticas
dc.subjectDecision tree learning
dc.subjectKnowledge discovery process
dc.subjectRegionalization
dc.subjectSpatial clustering
dc.subjectSpatial data mining
dc.titleKnowledge discovery process for description of spatially referenced clusters
dc.typeObjeto de conferencia
dc.typeObjeto de conferencia


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