<?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>2026-04-17T11:25:51Z</responseDate><request verb="GetRecord" identifier="oai:www.recercat.cat:2117/82923" metadataPrefix="oai_dc">https://recercat.cat/oai/request</request><GetRecord><record><header><identifier>oai:recercat.cat:2117/82923</identifier><datestamp>2026-01-30T03:15:49Z</datestamp><setSpec>com_2072_1033</setSpec><setSpec>col_2072_452950</setSpec></header><metadata><oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
   <dc:title>A weighted Cramér’s V Index for the assessment of stability in the fuzzy clustering of class C G protein-coupled receptors</dc:title>
   <dc:creator>Vellido Alcacena, Alfredo</dc:creator>
   <dc:creator>Halka, Christiana</dc:creator>
   <dc:creator>Nebot Castells, M. Àngela</dc:creator>
   <dc:contributor>Universitat Politècnica de Catalunya. Departament de Ciències de la Computació</dc:contributor>
   <dc:contributor>Universitat Politècnica de Catalunya. SOCO - Soft Computing</dc:contributor>
   <dc:subject>Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica</dc:subject>
   <dc:subject>Protein research</dc:subject>
   <dc:subject>Fuzzy clustering</dc:subject>
   <dc:subject>K-Means</dc:subject>
   <dc:subject>Clustering stability analysis</dc:subject>
   <dc:subject>Cramér’s V index</dc:subject>
   <dc:subject>G Protein-Coupled Receptors</dc:subject>
   <dc:subject>Proteïnes -- Investigació</dc:subject>
   <dc:description>After decades of intensive use, K-Means is still a common choice for crisp data clustering in real-world applications, particularly in biomedicine and bioinformatics. It is well-known that different initializations of the algorithm can lead to different solutions, precluding replicability. It has also been reported that even solutions with very similar errors may widely differ. A criterion for the choice of clustering solutions according to a combination of error and stability measures has recently been suggested. It is based on the use of Cramér’s V index, calculated from contingency tables, which is valid only for crisp clustering. Here, this criterion is extended to fuzzy and probabilistic clustering by first defining weighted contingency tables and a corresponding weighted Cramér’s V index. The proposed method is illustrated using Fuzzy C-Means in a proteomics problem.</dc:description>
   <dc:description>Peer Reviewed</dc:description>
   <dc:description>Postprint (author's final draft)</dc:description>
   <dc:date>2015</dc:date>
   <dc:type>Conference report</dc:type>
   <dc:identifier>Vellido, A., Halka, C., Nebot, M. A weighted Cramér’s V Index for the assessment of stability in the fuzzy clustering of class C G protein-coupled receptors. A: International Work-Conference on Bioinformatics and Biomedical Engineering. "Bioinformatics and Biomedical Engineering: Third International Conference, IWBBIO 2015, Granada, Spain, April 15-17, 2015: proceedings, part I (LNCS; 9043)". Granada: Springer, 2015, p. 536-547.</dc:identifier>
   <dc:identifier>978-3-319-16482-3</dc:identifier>
   <dc:identifier>https://hdl.handle.net/2117/82923</dc:identifier>
   <dc:identifier>10.1007/978-3-319-16483-0_52</dc:identifier>
   <dc:language>eng</dc:language>
   <dc:relation>http://link.springer.com/chapter/10.1007/978-3-319-16483-0_52</dc:relation>
   <dc:rights>Open Access</dc:rights>
   <dc:format>12 p.</dc:format>
   <dc:format>application/pdf</dc:format>
   <dc:publisher>Springer</dc:publisher>
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