Abstract
The next two chapters address classification issues from two varying perspectives. When considering groups of objects in a multivariate data set, two situations can arise. Given a data set containing measurements on individuals, in some cases we want to see if some natural groups or classes of individuals exist, and in other cases, we want to classify the individuals according to a set of existing groups. Cluster analysis develops tools and methods concerning the former case, that is, given a data matrix containing multivariate measurements on a large number of individuals (or objects), the objective is to build some natural sub-groups or clusters of individuals. This is done by grouping individuals that are “similar” according to some appropriate criterion.
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© 2015 Springer-Verlag Berlin Heidelberg
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Härdle, W.K., Simar, L. (2015). Cluster Analysis. In: Applied Multivariate Statistical Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45171-7_13
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DOI: https://doi.org/10.1007/978-3-662-45171-7_13
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