Sánchez-Díaz, GuillermoFranco-Arcega, AniluAguirre-Salado, CarlosPiza-Dávila, Hugo I.Morales-Manilla, LuisEscobar-Franco, Uriel2026-07-062026-07-062012-08Sánchez-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.978-3-642-32639-4https://hdl.handle.net/11117/12428This 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.enghttps://creativecommons.org/licenses/by-nc/4.0/deed.esK-most Similar NeighborK-nearest NeighborClassificationParallel Algorithmsinfo:eu-repo/semantics/article