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Picturing agreement between clustering solutions using multidimensional unfolding: An application to greenhouse gas emissions data

dc.contributor.authorMartins, Ana Alexandra
dc.contributor.authorCardoso, Maria Margarida
dc.date.accessioned2020-01-21T11:30:07Z
dc.date.available2020-01-21T11:30:07Z
dc.date.issued2020-02-01
dc.description.abstractWhen evaluating a clustering solution, we often have to compare alternative solutions - e.g., to address clustering stability or external validity. Each comparison essentially relies on a contingency table referring to a pair of (crisp) clustering solutions. These data is commonly used as an input to: (1) an assignment problem, to match the clusters of the two partitions; (2) determine several indices of agreement; (3) represent the two partitions in a two-dimensional map resorting to Correspondence Analysis. We propose using the Multidimensional Unfolding (MDU) technique to picture the cross-classification data between two partitions, complementing a clustering evaluation analysis and overcoming some limitations of the traditional approaches (1) to (3). This approach relies on a new similarity measure that excludes agreement between clusters due to chance alone. The resulting MDU map is very easy to interpret, picturing agreement between clustering solutions: the further apart are the clusters (represented by points) from the two partitions, the larger the (Euclidean) distances between the corresponding points. Two applications illustrate the relevance of this approach: an application to a data set on UCI Machine Learning Repository to access clustering external validity; and an application to greenhouse gas emissions data to address the temporal stability of clustering solutions, the clusters of European countries, which have homogeneous sources of pollutant emissions, being compared over three years.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMARTINS, Ana Alexandra A. F.; CARDOSO, Margarida G. M. S. – Picturing agreement between clustering solutions using multidimensional unfolding: An application to greenhouse gas emissions data. Journal of the Operational Research Society. ISSN 0160-5682. Vol. 71, N.º 2 (2020), pp. 195-208pt_PT
dc.identifier.doihttps://doi.org/10.1080/01605682.2018.1549648pt_PT
dc.identifier.issn0160-5682
dc.identifier.issn1476-9360
dc.identifier.urihttp://hdl.handle.net/10400.21/11009
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherTaylor & Francispt_PT
dc.relation.publisherversionhttps://www.tandfonline.com/doi/pdf/10.1080/01605682.2018.1549648?needAccess=truept_PT
dc.subjectMultidimensional unfoldingpt_PT
dc.subjectClustering evaluationpt_PT
dc.subjectIndices of agreementpt_PT
dc.subjectAssignmentpt_PT
dc.titlePicturing agreement between clustering solutions using multidimensional unfolding: An application to greenhouse gas emissions datapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage208pt_PT
oaire.citation.issue2pt_PT
oaire.citation.startPage195pt_PT
oaire.citation.titleJournal of the Operational Research Societypt_PT
oaire.citation.volume71pt_PT
person.familyNameMartins
person.familyNameCardoso
person.givenNameAna Alexandra
person.givenNameMaria Margarida
person.identifier.ciencia-id2016-88BF-5D0B
person.identifier.ciencia-id3E1B-1DAD-9287
person.identifier.orcid0000-0003-3733-6619
person.identifier.orcid0000-0001-6239-7283
person.identifier.scopus-author-id34969159800
person.identifier.scopus-author-id21233265300
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication7174415b-4387-4cc9-97b0-6c2a3e0c88b4
relation.isAuthorOfPublication069c85c4-ebe3-4293-b88a-dd066bc288de
relation.isAuthorOfPublication.latestForDiscovery7174415b-4387-4cc9-97b0-6c2a3e0c88b4

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