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dc.contributor.authorValÊncio, C. R.
dc.contributor.authorGuimarães, Diogo Lemos
dc.contributor.authorZafalon, Geraldo Francisco Donega
dc.contributor.authorNeves, Leandro Alves [UNESP]
dc.contributor.authorColombini, Angelo C.
dc.date.accessioned2016-03-02T13:04:25Z
dc.date.available2016-03-02T13:04:25Z
dc.date.issued2015
dc.identifierhttp://link.springer.com/chapter/10.1007/978-3-662-46078-8_46
dc.identifier.citationLecture Notes in Computer Science, v. 8939, p. 555-565, 2015.
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11449/135783
dc.description.abstractThe increase in new electronic devices had generated a considerable increase in obtaining spatial data information; hence these data are becoming more and more widely used. As well as for conventional data, spatial data need to be analyzed so interesting information can be retrieved from them. Therefore, data clustering techniques can be used to extract clusters of a set of spatial data. However, current approaches do not consider the implicit semantics that exist between a region and an object’s attributes. This paper presents an approach that enhances spatial data mining process, so they can use the semantic that exists within a region. A framework was developed, OntoSDM, which enables spatial data mining algorithms to communicate with ontologies in order to enhance the algorithm’s result. The experiments demonstrated a semantically improved result, generating more interesting clusters, therefore reducing manual analysis work of an expert.en
dc.description.abstractEste artigo foi publicado em Lecture Notes in Computer Science, a partir da apresentação do mesmo na 41st International Conference on Current Trends in Theory and Practice of Computer Science, Pec pod Sně kou, Czech Republic, January 24-29, 2015. Proceedingspt
dc.format.extent555-565
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science
dc.sourceCurrículo Lattes
dc.subjectData miningen
dc.subjectOntologyen
dc.subjectContext-awareen
dc.titleOntoSDM: an approach to improve quality on spatial data mining algorithmsen
dc.typeArtigo
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.description.affiliationUniversidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Ciência da Computação e Estatística, Instituto de Biociências Letras e Ciências Exatas de São José do Rio Preto, São José do Rio Preto, Rua Cristóvão Colombo, 2265, Jardim Nazareth, CEP 15054000, SP, Brasil
dc.description.affiliationUnespUniversidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Ciência da Computação e Estatística, Instituto de Biociências Letras e Ciências Exatas de São José do Rio Preto, São José do Rio Preto, Rua Cristóvão Colombo, 2265, Jardim Nazareth, CEP 15054000, SP, Brasil
dc.rights.accessRightsAcesso restrito
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Biociências Letras e Ciências Exatas, São José do Rio Pretopt
dc.identifier.lattes2139053814879312
unesp.departmentCiência da Computação e Estatísticapt
unesp.author.lattes2139053814879312
dc.relation.ispartofsjr0,295
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