Parallel k-most similar neighbor classifier for mixed data Conference Paper

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Overview

abstract

• This 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. © 2012 Springer-Verlag.

authors

• Aguirre Salado Carlos Arturo
• Escobar-Franco U.E.
• Franco-Arcega A.
• Morales-Manilla L.R.
• Piza-Dávila I.
• Sánchez Díaz Guillermo

publication date

• 2012-01-01

published in

• Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  Journal

Research

keywords

• classification; K-most similar neighbor; K-nearest neighbor; parallel algorithms Incremental algorithm; K-most similar neighbor; K-nearest neighbors; Large datasets; Main memory; Metric properties; Mixed data; Similarity functions; Synthetic and real data; Training data sets; Artificial intelligence; Classification (of information); Parallel algorithms; Algorithms

Identity

Digital Object Identifier (DOI)

• 10.1007/978-3-642-32639-4_71

Additional Document Info

start page

• 586

end page

• 593

volume

• 7435 LNCS