Multidimensional scaling

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The technique is also used in marketing, see Multidimensional scaling in marketing

Multidimensional scaling (MDS) is a set of related statistical techniques often used in data visualisation for exploring similarities or dissimilarities in data. An MDS algorithm starts with a matrix of item-item similarities, then assigns a location of each item in a low-dimensional space, suitable for graphing or 3D visualisation.

Applications include scientific visualisation and data mining in fields such as cognitive science, psychophysics, psychometrics and ecology.

MDS algorithms fall into a taxonomy, depending on the meaning of the input matrix:


References

  • Torgerson, W. S. (1958). Theory & Methods of Scaling. New York: Wiley.
  • Kruskal, J. B., and Wish, M. (1978), Multidimensional Scaling, Sage University Paper series on Quantitative Application in the Social Sciences, 07-011. Beverly Hills and London: Sage Publications.
  • Cox, M.F., Cox, M.A.A., (2001), Multidimensional Scaling, Chapman and Hall.
  • Coxon, Anthony P.M. (1982): "The User's Guide to Multidimensional Scaling. With special reference to the MDS(X) library of Computer Programs." London: Heinemann Educational Books.
  • Bronstein, A. M, Bronstein, M.M, and Kimmel, R. (2006), Generalized multidimensional scaling: a framework for isometry-invariant partial surface matching, Proc. National Academy of Sciences (PNAS), Vol. 103/5, pp. 1168-1172.

External links

it:Scaling multidimensionale de:Multidimensionale Skalierung zh:多维标度