Journal of Hebei University(Natural Science Edition) >

2016 , Vol. 36 >Issue 6: 650 - 656

DOI: https://doi.org/10.3969/j.issn.1000-1565.2016.06.013

Experimental comparison of two acceleration approaches for K-nearest neighbors

  • ZHAI Junhai ,
  • WANG Tingting ,
  • ZHANG Mingyang ,
  • WANG Yaoda ,
  • LIU Mingming
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  • College of Mathematics and Information Science, Hebei University, Baoding 071002, China

Received date: 2016-07-11

  Online published: 2016-11-25

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Abstract

K-NN(K-nearest neighbors)is a famous data mining algorithm with wide range of applications.The idea of K-NN is simple and it is easy to implement. Both computational time and space complexity of K-NN are all O(n),where,n is the number of instances in a training set.When K-NN encountered larger training sets,especially faced with big data sets,the efficiency of K-NN becomes very low,even K-NN is impracticable.Two acceleration approaches for K-nearest neighbors are experimentally compared on 8 data sets.The two acceleration approaches are the CNN and MapReduce based K-NN.Specifically,in Hadoop environment,this paper implements K-NN with MapReduce,and experimentally compares with CNN on 8 data sets. Some valuable conclusions are obtained,and may be useful for researchers in related fields.

Key words： K-nearest neighbors; data mining; MapReduce; Hadoop

Cite this article

ZHAI Junhai , WANG Tingting , ZHANG Mingyang , WANG Yaoda , LIU Mingming . Experimental comparison of two acceleration approaches for K-nearest neighbors[J]. Journal of Hebei University(Natural Science Edition), 2016 , 36(6) : 650 -656 . DOI: 10.3969/j.issn.1000-1565.2016.06.013

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