Clustering algorithm based on asymmetric similarity and paradigmatic features

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dc.contributor.authorSantisteban Pablo, Julio Omar
dc.contributor.authorTejada Cárcamo, Javier
dc.date.accessioned2019-01-29T22:19:53Z
dc.date.available2019-01-29T22:19:53Z
dc.date.issued2016
dc.description.abstractSimilarity measures are essential to solve many pattern recognition problems such as classification, clustering, and information retrieval. Various similarity measures are categorised in both syntactic and semantic relationships. In this paper, we present a novel similarity, unilateral Jaccard similarity coefficient (uJaccard), which does not only take into consideration the space among two points but also the semantics among them. How can we retrieve meaningful information from a large and sparse graph? Traditional approaches focus on generic clustering techniques for network graph. However, they tend to omit interesting patterns such as the paradigmatic relations. In this paper, we propose a novel graph clustering technique modelling the relations of a node using the paradigmatic analysis. Our proposed algorithm paradigmatic clustering (PaC) for graph clustering uses paradigmatic analysis supported by an asymmetric similarity using uJaccard. Extensive experiments and empirical analysis are used to evaluate our algorithm on synthetic and real data. Copyright © 2016 Inderscience Enterprises Ltd.es_PE
dc.description.uriTrabajo de investigaciónes_PE
dc.identifier.doihttps://doi.org/10.1504/IJICA.2016.080871es_PE
dc.identifier.issn1751648Xes_PE
dc.identifier.urihttps://hdl.handle.net/20.500.12590/15845
dc.language.isoenges_PE
dc.publisherInderscience Enterprises Ltd.es_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85005769363&doi=10.1504%2fIJICA.2016.080871&partnerID=40&md5=f1f371ea2377f24f6bb635a2ad0d180aes_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectClassification (of information)es_PE
dc.subjectCluster analysises_PE
dc.subjectGraph theoryes_PE
dc.subjectPattern recognitiones_PE
dc.subjectSemanticses_PE
dc.subjectAsymmetric similarityes_PE
dc.subjectJaccard similarity coefficientses_PE
dc.subjectParadigmatic similarityes_PE
dc.subjectPattern recognition problemses_PE
dc.subjectSemantic relationshipses_PE
dc.subjectSimilarityes_PE
dc.subjectSynthetic and real dataes_PE
dc.subjectTraditional approacheses_PE
dc.subjectClustering algorithmses_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.02.00es_PE
dc.titleClustering algorithm based on asymmetric similarity and paradigmatic featureses_PE
dc.typeinfo:eu-repo/semantics/article
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