Numerical Data Clustering Ontology Approach

dc.contributor.authorGrabusts, Peter
dc.coverage.issue1cs
dc.coverage.volume24cs
dc.date.accessioned2019-06-26T10:18:35Z
dc.date.available2019-06-26T10:18:35Z
dc.date.issued2018-06-01cs
dc.description.abstractClustering algorithm tasks are used to group given objects defined by a set of numerical properties in such a way that the objects within a group are more similar than the objects in different groups. All clustering algorithms have common parameters the choice of which characterizes the effectiveness of clustering. The most important parameters characterizing clustering are: metrics, number of clusters and cluster validity criteria. In classic clustering algorithms semantic knowledge is ignored. This creates difficulties in interpreting the results of clustering. At present, the use of ontology opportunities is developing very rapidly, that provide an explicit model for structuring concepts, together with their interrelationship, which allows you to gain knowledge of a particular data model. According to the previously obtained results of clustering study, the author will make an attempt to create ontology-based concept from numerical data using similarity measures, cluster numbers, cluster validity and others characteristic features. To scientific novelty should be attributed the combination of approaches of classical data analysis and ontological approach to their structuring, that increases the efficiency of their use in engineering practice.en
dc.formattextcs
dc.format.extent31-38cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMendel. 2018 vol. 24, č. 1, s. 31-38. ISSN 1803-3814cs
dc.identifier.doi10.13164/mendel.2018.1.031en
dc.identifier.issn2571-3701
dc.identifier.issn1803-3814
dc.identifier.urihttp://hdl.handle.net/11012/179221
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/17cs
dc.rights.accessopenAccessen
dc.subjectClusteringen
dc.subjectCluster analysisen
dc.subjectOntologyen
dc.titleNumerical Data Clustering Ontology Approachen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
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