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dc.creatorTrejos Zelaya, Javier
dc.creatorAmaya Briceño, Luis Eduardo
dc.creatorJiménez Romero, Alejandra
dc.creatorMurillo Fernández, Alex
dc.creatorPiza Volio, Eduardo
dc.creatorVillalobos Arias, Mario Alberto
dc.date.accessioned2020-09-21T16:18:16Z
dc.date.available2020-09-21T16:18:16Z
dc.date.issued2019-08-09
dc.identifier.citationhttps://arxiv.org/abs/2001.01809es_ES
dc.identifier.urihttps://hdl.handle.net/10669/81593
dc.descriptionArtículo será publicado en Springer Verlag, como capítulo del libro "Data Analysis and Rationality in a Complex World".es_ES
dc.description.abstractWe study clustering methods for binary data, first defining aggregation criteria that measure the compactness of clusters. Five new and original methods are introduced, using neighborhoods and population behavior combinatorial optimization metaheuristics: first ones are simulated annealing, threshold accepting and tabu search, and the others are a genetic algorithm and ant colony optimization. The methods are implemented, performing the proper calibration of parameters in the case of heuristics, to ensure good results. From a set of 16 data tables generated by a quasi-Monte Carlo experiment, a comparison is performed for one of the aggregations using L1 dissimilarity, with hierarchical clustering, and a version of k-means: partitioning around medoids or PAM. Simulated annealing perform very well, especially compared to classical methods.es_ES
dc.description.sponsorshipUniversidad de Costa Ricaes_ES
dc.description.sponsorshipInstituto Tecnológico de Costa Ricaes_ES
dc.language.isoen_USes_ES
dc.subjectclusteringes_ES
dc.subjectbinary dataes_ES
dc.subjectSimulated annealinges_ES
dc.subjectthreshold acceptinges_ES
dc.subjecttabu searches_ES
dc.subjectGenetic algorithmes_ES
dc.subjectAnt colonyes_ES
dc.titleClustering binary data by application of combinatorial optimization heuristicses_ES
dc.typedocumento de trabajoes_ES
dc.description.procedenceUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones en Matemáticas Puras y Aplicadas (CIMPA)es_ES


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