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Cluster Analysis and Multidimensional Scaling

  • J. D. Jobson
Part of the Springer Texts in Statistics book series (STS)

Abstract

This chapter continues the discussion of data reduction techniques begun in Chapter 9. In Chapter 9 the focus was on reducing the number of variables or columns of the data matrix X. Chapter 10 begins by focusing on the reduction of the number of rows of X. Since the rows of X represent observational units, the approach is to combine the units into groups of relatively homogeneous units called clusters. For this approach to data reduction, the various techniques available are commonly called cluster analysis.

Keywords

Multidimensional Scaling Cluster Solution Single Linkage Dissimilarity Matrix Proximity Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • J. D. Jobson
    • 1
  1. 1.Faculty of BusinessUniversity of AlbertaEdmontonCanada

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