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Cluster Analysis

In: Applied Multivariate Statistical Analysis

Author

Listed:
  • Wolfgang Karl Härdle

    (Humboldt-Universität zu Berlin, Ladislaus von Bortkiewicz Chair of Statistics)

  • Léopold Simar

    (Université Catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences)

  • Matthias R. Fengler

    (University of St. Gallen, School of Economics and Political Science)

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 data 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.

Suggested Citation

  • Wolfgang Karl Härdle & Léopold Simar & Matthias R. Fengler, 2024. "Cluster Analysis," Springer Books, in: Applied Multivariate Statistical Analysis, edition 0, chapter 0, pages 373-406, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-63833-6_13
    DOI: 10.1007/978-3-031-63833-6_13
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