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Published in:
2012 | OriginalPaper | Chapter
Cluster Analysis
Authors : Wolfgang Karl Härdle, Léopold Simar
Published in: Applied Multivariate Statistical Analysis
Publisher: Springer Berlin Heidelberg
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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 subgroups or clusters of individuals. This is done by grouping individuals that are “similar” according to some appropriate criterion. Once the clusters are obtained, it is generally useful to describe each group using some descriptive tool from Chapters
1
,
9
or
10
to create a better understanding of the differences that exist among the formulated groups.