Journal of the Korean Statistical Society

The Korean Statistical Society (한국통계학회)
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On a Modified k-spatial Medians Clustering

  • Published : 2000.06.01
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Abstract

This paper is concerned with a modification of the k-spatial medians clustering. To find a suitable number of clusters, the number k of clusters is incorporated into the k-spatial medians clustering criterion through a weight function. Proposed method for the choice of the weight function offers a reasonable number of clusters. Some theoretical properties of the method are investigated along with some examples.

Keywords

References

  1. J. R. Statist. Soc. v.B 45 Statistical uses of the spatial median Brown, B.M.
  2. Journal of Multivariate Analysis v.18 Optimal stratification and clustering on the line using the L₁-norm Butler, R.W.
  3. Data analysis and informatics v.Ⅴ On a modification of the k-means clustering procedure Gaenssler, P.
  4. Clustering Algorithms Hartigan, J.A.
  5. Annals of Statistics v.6 Asymtotic distributions for clustering criteria Hartigan, J.A.
  6. Journal of the Korean Statistical Society v.15 Bootstrap Method for k-Spatial Medians Jhun, M.
  7. Biometrics v.44 A criterion for determining the number of groups in a data set using sum-of-squares clustering Krzanowski, W.J.;Lai, Y.T.
  8. Biometrics v.27 Practical problems in a method of cluster analysis Marriott, F.H.C.
  9. Psychometrika v.50 An examination of procedures for determining the number of clusters in a data set Milligan, G.W.;Cooper, M.C.
  10. Journal of the American Statistical Association v.84 Approximate confidence intervals for the number of clusters Peck, R.;Fisher, L.;van Ness, J.
  11. Annals of Statistics v.9 Strong consistency of k-means clustering Pollard, D.
  12. Annals of Probability v.10 A central limit theorem for k-means clustering Pollard, D.
  13. Convergence of Stochastic Processes Pollard, D.
  14. Journal of Statistical Computing and Simulation v.53 A simulation study of the weighted k-means cluster procedure Rost, D.
  15. Clustering Analysis Algorithms for data reduction and classification of objects Spath, H.