Advanced Techniques in Computing Sciences and Software Engineering

pp 79-84


Comparative Study of Distance Functions for Nearest Neighbors

  • Janett Walters-WilliamsAffiliated withSchool of Computing and Information Technology, University of Technology Email author 
  • , Yan LiAffiliated withDepartment of Mathematics and Computing Centre for Systems Biology, University of Southern Queensland

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Many learning algorithms rely on distance metrics to receive their input data. Research has shown that these metrics can improve the performance of these algorithms. Over the years an often popular function is the Euclidean function. In this paper, we investigate a number of different metrics proposed by different communities, including Mahalanobis, Euclidean, Kullback-Leibler and Hamming distance. Overall, the best-performing method is the Mahalanobis distance metric.


Kullback-Leibler distance Euclidean distance Mahalanobis distance Manhattan distance Hamming distance Minkowski distance Nearest Neighbor