Abstract
The most frequently used hierarchical methods for clustering of quantitative variables are based on bivariate or multivariate correlation measures. These solutions can be unsuitable in presence of uncorrelated but collinear variables. In this paper we propose a hierarchical agglomerative algorithm based on a similarity measure which takes into account the collinearity between two groups of variables. Its main theoretical features are described and its performance is evaluated both on simulated and real data sets.
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© 2005 Springer-Verlag Berlin · Heidelberg
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Laghi, A., Soffritti, G. (2005). A Collinearity Based Hierarchical Method to Identify Clusters of Variables. In: Bock, HH., et al. New Developments in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27373-5_7
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DOI: https://doi.org/10.1007/3-540-27373-5_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23809-6
Online ISBN: 978-3-540-27373-8
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