Parallel k-Most Similar Neighbor Classifier for Mixed Data

  • Guillermo Sanchez-Diaz
  • Anilu Franco-Arcega
  • Carlos Aguirre-Salado
  • Ivan Piza-Davila
  • Luis R. Morales-Manilla
  • Uriel Escobar-Franco
Conference paper

DOI: 10.1007/978-3-642-32639-4_71

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7435)
Cite this paper as:
Sanchez-Diaz G., Franco-Arcega A., Aguirre-Salado C., Piza-Davila I., Morales-Manilla L.R., 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

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.

Keywords

K-most similar neighbor K-nearest neighbor classification parallel algorithms 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Guillermo Sanchez-Diaz
    • 1
  • Anilu Franco-Arcega
    • 2
  • Carlos Aguirre-Salado
    • 1
  • Ivan Piza-Davila
    • 3
  • Luis R. Morales-Manilla
    • 4
  • Uriel Escobar-Franco
    • 4
  1. 1.Universidad Autonoma de San Luis PotosiSan Luis PotosiMexico
  2. 2.Universidad Autonoma del Estado de HidalgoPachucaMexico
  3. 3.Instituto Tecnologico y de Estudios Superiores de OccidenteTlaquepaqueMexico
  4. 4.Universidad Politecnica de TulancingoTulancingoMexico

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