Memberships Networks for High-Dimensional Fuzzy Clustering Visualization

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dc.citation.journalTitleCommunications in Computer and Information Scienceeng
dc.contributor.authorAriza-Jiménez L.
dc.contributor.authorVilla L.F.
dc.contributor.authorQuintero O.L.
dc.contributor.departmentUniversidad EAFIT. Escuela de Cienciasspa
dc.contributor.researchgroupModelado Matemáticospa
dc.date.accessioned2021-04-12T14:07:18Z
dc.date.available2021-04-12T14:07:18Z
dc.date.issued2019-01-01
dc.description.abstractVisualizing the cluster structure of high-dimensional data is a non-trivial task that must be able to deal with the large dimensionality of the input data. Unlike hard clustering structures, visualization of fuzzy clusterings is not as straightforward because soft clustering algorithms yield more complex clustering structures. Here is introduced the concept of membership networks, an undirected weighted network constructed based on the fuzzy partition matrix that represents a fuzzy clustering. This simple network-based method allows understanding visually how elements involved in this kind of complex data clustering structures interact with each other, without relying on a visualization of the input data themselves. Experiment results demonstrated the usefulness of the proposed method for the exploration and analysis of clustering structures on the Iris flower data set and two large and unlabeled financial datasets, which describes the financial profile of customers of a local bank. © 2019, Springer Nature Switzerland AG.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=9897
dc.identifier.doi10.1007/978-3-030-31019-6_23
dc.identifier.issn18650929
dc.identifier.issn18650937
dc.identifier.otherWOS;000525351100023
dc.identifier.otherSCOPUS;2-s2.0-85075665935
dc.identifier.urihttp://hdl.handle.net/10784/27813
dc.language.isoengeng
dc.publisherSpringer Verlag
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85075665935&doi=10.1007%2f978-3-030-31019-6_23&partnerID=40&md5=cc5318918e57cf763413520330bcc88a
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/1865-0929
dc.sourceCommunications in Computer and Information Science
dc.subject.keywordCluster analysiseng
dc.subject.keywordComplex networkseng
dc.subject.keywordData visualizationeng
dc.subject.keywordFuzzy clusteringeng
dc.subject.keywordInput output programseng
dc.subject.keywordLarge dataseteng
dc.subject.keywordVisualizationeng
dc.subject.keywordCluster structureeng
dc.subject.keywordFinancial profileseng
dc.subject.keywordHard clusteringeng
dc.subject.keywordHigh dimensional dataeng
dc.subject.keywordHigh-dimensionaleng
dc.subject.keywordNon-trivial taskseng
dc.subject.keywordSimple networkseng
dc.subject.keywordWeighted networkseng
dc.subject.keywordClustering algorithmseng
dc.titleMemberships Networks for High-Dimensional Fuzzy Clustering Visualizationeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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