Generalised procrustes analysis with optimal scaling: Exploring data from a power supplier

J.E. Wieringa, G.B. Dijksterhuis*, J.C. Gower, F. van Perlo-ten Kleij

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

3 Citaten (Scopus)


Generalised Procrustes Analysis (GPA) is a method for matching several, possibly large, data sets by fitting them to each other using transformations, typically rotations. The linear version of GPA has been applied in a wide range of contexts. A non-linear extension of GPA is developed which uses Optimal Scaling (OS). The approach is suited to match data sets that contain nominal variables. A database of a Dutch power supplier that contains many categorical variables unfit for the usual linear GPA methodology is used to illustrate the approach. (c) 2009 Elsevier B.V. All rights reserved.

Originele taal-2English
Pagina's (van-tot)4546-4554
Aantal pagina's9
TijdschriftComputational Statistics and Data Analysis
Nummer van het tijdschrift12
StatusPublished - 1-okt.-2009

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