Abstract
This paper shows how methods of cluster analysis, principal component analysis, and multidimensional scaling may be combined in order to obtain an optimal fit between a classification underlying some set of objects 1,…,n and its visual representation in a low-dimensional euclidean space ℝs. We propose several clustering criteria and corresponding k-means-like algorithms which are based either on a probabilistic model or on geometrical considerations leading to matrix approximation problems. In particular, a MDS-clustering strategy is presented for-displaying not only the n objects using their pairwise dissimilarities, but also the detected clusters and their average distances.
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© 1987 D. Reidel Publishing Company, Dordrecht, Holland
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Bock, H.H. (1987). On the Interface between Cluster Analysis, Principal Component Analysis, and Multidimensional Scaling. In: Bozdogan, H., Gupta, A.K. (eds) Multivariate Statistical Modeling and Data Analysis. Theory and Decision Library, vol 8. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-3977-6_2
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DOI: https://doi.org/10.1007/978-94-009-3977-6_2
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