In contrast to cluster analysis, ordination orders quantities in a (usually lower-dimensional) latent space.
In the ordination space, quantities that are near each other share attributes (i.e., are similar to some degree), and dissimilar objects are farther from each other.
Such relationships between the objects, on each of several axes or latent variables, are then characterized numerically and/or graphically in a biplot.
The third group includes model-based ordination methods, which can be considered as multivariate extensions of Generalized Linear Models.
These and other assumptions, such as the assumed mean-variance relationship, can be validated with the use of residual diagnostics, unlike in other ordination methods.