Parallel k-Most Similar Neighbor Classifier for Mixed Data

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dc.contributor.authorSánchez-Díaz, Guillermo
dc.contributor.authorFranco-Arcega, Anilu
dc.contributor.authorAguirre-Salado, Carlos
dc.contributor.authorPiza-Dávila, Hugo I.
dc.contributor.authorMorales-Manilla, Luis
dc.contributor.authorEscobar-Franco, Uriel
dc.date.accessioned2026-07-06T19:20:30Z
dc.date.available2026-07-06T19:20:30Z
dc.date.issued2012-08
dc.description.abstractThis paper presents a paralellization of the incremental algorithm inc-k-msn, for mixed data and similarity functions that do not satisfy metric properties. The algorithm presented is suitable for processing large data sets, because it only stores in main memory the k-most similar neighbors processed in step t, traversing only once the training data set. Several experiments with synthetic and real data are presented.
dc.description.sponsorshipITESO, A.C.es_MX
dc.identifier.citationSánchez-Díaz, G.; Franco-Arcega, A.; Aguirre-Salado, C.; Piza-Dávila, H.I.; Morales-Manilla, L.; Escobar-Franco, U. (2012). Parallel k-Most Similar Neighbor Classifier for Mixed Data. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg.
dc.identifier.isbn978-3-642-32639-4
dc.identifier.urihttps://hdl.handle.net/11117/12428
dc.language.isoeng
dc.publisherSpringer Nature
dc.relation.ispartofseriesIntelligent Data Engineering and Automated Learning - IDEAL 2012
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/deed.es
dc.subjectK-most Similar Neighbor
dc.subjectK-nearest Neighbor
dc.subjectClassification
dc.subjectParallel Algorithms
dc.titleParallel k-Most Similar Neighbor Classifier for Mixed Data
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion

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