Abstract
This paper addresses the problem of performing a hierarchical cluster analysis on objects that are measured on the same variable on a number of equally spaced points. Such data are typically collected in longitudinal studies or in experiments where electro-physiological measurements are registered (such as EEG or EMG). A generalized inter-object distance measure is defined that takes into account various aspects of the similarity between the functions from which the data are sampled. A mathematical programming procedure is developed for weighting these aspects in such a way that the resulting inter-object distances optimally satisfy the ultrametric inequality. These optimally weighted distances can then be subjected to any existing hierarchical clustering procedure. The new approach is illustrated on an artificial data set and some possible limitations and extensions of the new method are discussed.
Supported as “Bevoegdverklaard Navorser” of the Belgian “Nationaal Fonds voor Wetenschappelijk Onderzoek”.
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© 1993 Springer-Verlag Berlin · Heidelberg
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De Soete, G. (1993). Hierarchical Clustering of Sampled Functions. In: Opitz, O., Lausen, B., Klar, R. (eds) Information and Classification. Studies in Classification, Data Analysis and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-50974-2_1
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DOI: https://doi.org/10.1007/978-3-642-50974-2_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-56736-3
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