<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-04-17T12:40:25Z</responseDate><request verb="GetRecord" identifier="oai:www.recercat.cat:2117/19223" metadataPrefix="qdc">https://recercat.cat/oai/request</request><GetRecord><record><header><identifier>oai:recercat.cat:2117/19223</identifier><datestamp>2026-02-01T06:08:34Z</datestamp><setSpec>com_2072_1033</setSpec><setSpec>col_2072_452950</setSpec></header><metadata><qdc:qualifieddc xmlns:qdc="http://dspace.org/qualifieddc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://purl.org/dc/elements/1.1/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dc.xsd http://purl.org/dc/terms/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dcterms.xsd http://dspace.org/qualifieddc/ http://www.ukoln.ac.uk/metadata/dcmi/xmlschema/qualifieddc.xsd">
   <dc:title>How to make T-transitive a proximity relation</dc:title>
   <dc:creator>Garmendia Salvador, Luis</dc:creator>
   <dc:creator>Recasens Ferrés, Jorge</dc:creator>
   <dc:subject>Àrees temàtiques de la UPC::Matemàtiques i estadística::Lògica matemàtica</dc:subject>
   <dc:subject>Fuzzy logic</dc:subject>
   <dc:subject>Lògica difusa</dc:subject>
   <dcterms:abstract>Three ways to approximate a proximity relation R (i.e., a reflexive and symmetric fuzzy relation) by a T -transitive one where T is a continuous Archimedean t-norm are given. The first one aggregates the transitive closure R macr of R with a (maximal) T-transitive relation B contained in R . The second one computes the closest homotecy of R macr or B to better fit their entries with the ones of R. The third method uses nonlinear programming techniques to obtain the best approximation with respect to the Euclidean distance for T the Lukasiewicz or the product t-norm. The previous methods do not apply for the minimum t-norm. An algorithm to approximate a given proximity relation by a min-transitive relation (a similarity) is given in the last section of the paper.</dcterms:abstract>
   <dcterms:abstract>Peer Reviewed</dcterms:abstract>
   <dcterms:abstract>Postprint (published version)</dcterms:abstract>
   <dcterms:issued>2009-02</dcterms:issued>
   <dc:type>Article</dc:type>
   <dc:relation>http://ieeexplore.ieee.org/xpl/aboutJournal.jsp?punumber=91</dc:relation>
   <dc:rights>Restricted access - publisher's policy</dc:rights>
</qdc:qualifieddc></metadata></record></GetRecord></OAI-PMH>