Multidimensional Scaling

Multidimensional scaling (MDS) is a set of related statistical techniques often used in data visualisation for exploring similarities or dissimilarities in data. MDS is a special case of ordination. 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.
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a multivariate statistical technique concerned mainly with the relationships, differences, similarities, interaction, substitutability, etc. among behavioural data.

Multidimensional scaling (MDS) can be considered to be an alternative to factor analysis (see Factor Analysis), and it is typically used as an exploratory method. In general, the goal of the analysis is to detect meaningful underlying dimensions that allow the researcher to explain observed similarities or dissimilarities (distances) between the investigated objects. In factor analysis, the similarities between objects (e.g., variables) are expressed in the correlation matrix. With MDS one may analyze not only correlation matrices but also any kind of similarity or dissimilarity matrix (including sets of measures that are not internally consistent, e.g., do not follow the rule of transitivity).
For more information, see the Multidimensional Scaling chapter.