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Memberships Networks for High-Dimensional Fuzzy Clustering Visualization

dc.creatorAriza-Jiménez L.
dc.creatorVilla L.F.
dc.creatorQuintero O.L.
dc.date2019
dc.date.accessioned2020-04-29T14:53:35Z
dc.date.available2020-04-29T14:53:35Z
dc.identifier.isbn9783030310189
dc.identifier.issn18650929
dc.identifier.urihttp://hdl.handle.net/11407/5662
dc.descriptionVisualizing 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.
dc.language.isoeng
dc.publisherSpringer
dc.relation.isversionofhttps://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.sourceCommunications in Computer and Information Science
dc.subjectClustering visualization
dc.subjectFuzzy clustering
dc.subjectHigh-dimensional data
dc.subjectMembership network
dc.subjectCluster analysis
dc.subjectComplex networks
dc.subjectData visualization
dc.subjectFuzzy clustering
dc.subjectInput output programs
dc.subjectLarge dataset
dc.subjectVisualization
dc.subjectCluster structure
dc.subjectFinancial profiles
dc.subjectHard clustering
dc.subjectHigh dimensional data
dc.subjectHigh-dimensional
dc.subjectNon-trivial tasks
dc.subjectSimple networks
dc.subjectWeighted networks
dc.subjectClustering algorithms
dc.titleMemberships Networks for High-Dimensional Fuzzy Clustering Visualization
dc.typeConference Papereng
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Sistemas
dc.identifier.doi10.1007/978-3-030-31019-6_23
dc.relation.citationvolume1052
dc.relation.citationstartpage263
dc.relation.citationendpage273
dc.publisher.facultyFacultad de Ingenierías
dc.affiliationAriza-Jiménez, L., Mathematical Modeling Research Group, Universidad EAFIT, Medellín, Colombia; Villa, L.F., System Engineering Research Group, ARKADIUS, Universidad de Medellín, Medellín, Colombia; Quintero, O.L., Mathematical Modeling Research Group, Universidad EAFIT, Medellín, Colombia
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dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/article


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