Cluster Analysis

Klaus Backhaus (), Bernd Erichson (), Sonja Gensler (), Rolf Weiber () and Thomas Weiber
Additional contact information
Klaus Backhaus: University of Münster
Bernd Erichson: Otto-von-Guericke-University Magdeburg
Sonja Gensler: University of Münster
Rolf Weiber: University of Trier

Chapter Chapter 8 in Multivariate Analysis, 2021, pp 451-530 from Springer

Abstract: Abstract Cluster analysis is a procedure for grouping cases (objects of investigation) in a data set. For this purpose, the first step is to determine the similarity or dissimilarity (distance) between the cases by a suitable measure. The second step searches for the fusion algorithm which combines the individual cases successively into groups (clusters). The goal is to combine such cases into groups which are similar with respect to the considered segmentation variables (homogenous groups). At the same time, the groups should be as dissimilar as possible. The procedures of cluster analysis can handle variables with metric, non-metric as well as mixed scales. The focus of the chapter is on hierarchical agglomerative clustering methods, with the single-linkage method and Ward’s method presented in detail. Finally, k-means clustering and two-step cluster analysis, two partitioning cluster methods, are also explained. These methods offer particular advantages when working with large amounts of data.

Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-658-32589-3_8

Ordering information: This item can be ordered from
http://www.springer.com/9783658325893

DOI: 10.1007/978-3-658-32589-3_8

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().