ICE: A visual analytic tool for interactive clustering ensembles generation

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dc.contributor.authorCastro-Ochante, J.
dc.contributor.authorCamara-Chavez, G.
dc.contributor.authorGomez-Nieto, E.
dc.date.accessioned2022-03-12T03:33:53Z
dc.date.available2022-03-12T03:33:53Z
dc.date.issued2021
dc.description.abstract"Clustering methods are the most used algorithms for unsupervised learning. However, there is no unique optimal approach for all datasets since different clustering algorithms produce different partitions. To overcome this issue of selecting an appropriate technique and its corresponding parameters, cluster ensemble strategies are used for improving accuracy and robustness by a weighted combination of two or more approaches. However, this process is often carried out almost in a blind manner, testing different combinations of methods and assessing if its performance is beneficial for the defined purpose. Thus, the procedure for selecting the best combination tests many clustering ensembles until the desired result is achieved. This paper proposes a novel analytic tool for clustering ensemble generation, based on quantitative metrics and interactive visual resources. Our approach allows the analysts to display different results from state-of-the-art clustering methods and analyze their performance based on specific metrics and visual inspection. Based on their requirements/experience, the analysts can interactively assign weights to the different methods to set their contributions and manage (create, store, compare, and merge), such as for ensembles. Our approach's effectiveness is shown through a set of experiments and case studies, attesting to its usefulness in practical applications. © 2021 ACM."es_PE
dc.description.uriTrabajo académicoes_PE
dc.identifier.doi10.1145/3412841.3441921es_PE
dc.identifier.isbn9781450381048es_PE
dc.identifier.urihttps://hdl.handle.net/20.500.12590/17093
dc.language.isoenges_PE
dc.publisherAssociation for Computing Machineryes_PE
dc.publisher.countryPEes_PE
dc.relationinfo:eu-repo/semantics/articlees_PE
dc.relation.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85104949446&origin=resultslist&sort=plf-f&src=s&nlo=&nlr=&nls=&sid=7dfce5cde9eda76399a7c5cb6b0ab407&sot=aff&sdt=cl&cluster=scopubyr%2c%222021%22%2ct&sl=48&s=AF-ID%28%22Universidad+Cat%c3%b3lica+San+Pablo%22+60105300%29&relpos=23&citeCnt=0&searchTerm=&featureToggles=FEATURE_NEW_DOC_DETAILS_EXPORT:1es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.subjectClustering ensemblees_PE
dc.subjectMachine learninges_PE
dc.subjectVisual analyticses_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.02.01es_PE
dc.titleICE: A visual analytic tool for interactive clustering ensembles generationes_PE
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_PE
renati.typehttps://purl.org/pe-repo/renati/type#trabajoAcademico
thesis.degree.disciplineCiencia de la Computaciónes_PE
thesis.degree.grantorUniversidad Católica San Pablo. Departamento de Ciencia de la Computaciónes_PE
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