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A p-median problem with distance selection

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dc.affiliation.dptoUC3M. Departamento de EstadĂ­sticaes
dc.contributor.authorBenati, Stefano
dc.contributor.authorGarcĂ­a, Sergio
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de EstadĂ­stica
dc.date.accessioned2012-06-25T11:19:46Z
dc.date.available2012-06-25T11:19:46Z
dc.date.issued2012-06
dc.description.abstractThis paper introduces an extension of the p-median problem and its application to clustering, in which the distance/dissimilarity function between units is calculated as the distance sum on the q most important variables. These variables are to be chosen from a set of m elements, so a new combinatorial feature has been added to the problem, that we call the p-median model with distance selection. This problem has its origin in cluster analysis, often applied to sociological surveys, where it is common practice for a researcher to select the q statistical variables they predict will be the most important in discriminating the statistical units before applying the clustering algorithm. Here we show how this selection can be formulated as a non-linear mixed integer optimization mode and we show how this model can be linearized in several different ways. These linearizations are compared in a computational study and the results outline that the radius formulation of the p-median is the most efficient model for solving this problem.
dc.format.mimetypeapplication/pdf
dc.identifier.repecws121913
dc.identifier.urihttp://hdl.handle.net/10016/14672
dc.identifier.uxxiDT/0000000965
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries12-13
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEstadĂ­stica
dc.subject.otherp-median problem
dc.subject.otherDistance selection
dc.subject.otherRadius formulation
dc.titleA p-median problem with distance selection
dc.typeworking paper*
dc.type.hasVersionSMUR*
dspace.entity.typePublication
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