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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15788
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dc.contributor.authorRodríguez Siu, Kevin Christian-
dc.contributor.authorBarrios Aranibar, Dennis-
dc.contributor.authorPatiño Escarcina, Raquel Esperanza-
dc.date.accessioned2019-01-29T22:19:50Z-
dc.date.available2019-01-29T22:19:50Z-
dc.date.issued2017-
dc.identifier.isbn9781509051052es_PE
dc.identifier.urihttp://repositorio.ucsp.edu.pe/handle/UCSP/15788-
dc.description.abstractThe choice of a good clustering algorithm is vital in many tasks to optimize results. Nowadays, the most used algorithms use only one strategy to find and form the clusters of data, which can limit the effectiveness of the process. This paper presents a new approximation to clustering, called Essence-Based Clustering, that combines multiple strategies in a series of steps, allowing two levels of configuration of parameters, both for the whole algorithm and for each strategy used on its own. Experimental results in known data repositories show that this approach is well suited for solving clustering problems and it can do it with equivalent or better results than the current approaches. © 2016 IEEE.es_PE
dc.description.uriTrabajo de investigaciónes_PE
dc.language.isoenges_PE
dc.publisherInstitute of Electrical and Electronics Engineers Inc.es_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85018172586&doi=10.1109%2fLA-CCI.2016.7885724&partnerID=40&md5=d0dc545ae3c2b969b5c229f55d792cabes_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectApproximation algorithmses_PE
dc.subjectArtificial intelligencees_PE
dc.subjectBased clusteringes_PE
dc.subjectClustering approaches_PE
dc.subjectClustering problemses_PE
dc.subjectCustomizablees_PE
dc.subjectData repositorieses_PE
dc.subjectMulti-strategices_PE
dc.subjectMultiple strategyes_PE
dc.subjectClustering algorithmses_PE
dc.titleEssence-Based Clustering: A multi-strategic and highly-customizable clustering approaches_PE
dc.typeinfo:eu-repo/semantics/conferenceObjectes_PE
dc.identifier.doi10.1109/LA-CCI.2016.7885724es_PE
Appears in Collections:Artículos de investigación

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