Procrustes analysis

The program performs a least-squares orthogonal generalized Procrustes analysis (least-squares orthogonal mapping). Procrustes analysis is a method of comparing two sets of data. The method is based on matching corresponding points (landmarks) from each of the two data sets.

Landmarks are points that accurately describe a shape. Corresponding landmarks would be the same landmark on two different shapes.

Procrustes analysis is a rigid shape analysis that uses isomorphic scaling, translation, and rotation to find the ôbestö fit between two or more landmarked shapes.
See wikipedia for generalized orthogonal Procrustes analysis, and 'Procrustes Analysis' by Amy Ross, www.cse.sc.edu.

FindGraph uses scaling, translation, rotation, and additionally stretching/compressing and shearing transformations. It applies nonlinear mapping algorithm to find best fit, i.e. to find a reference cluster of landmarks, so that the distance of each reference landmark to it's corresponding 'experimental' landmark is minimised. Data points can be given greater or less influence over the Procrustes analysis by assigning a weight to each point.

To you define a reference cluster of landmarks you have to select the data series and to define N marks.

There are different ways to define an 'experimental' cluster of landmarks:


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