Cluster Analysis

Thomas Cleff ()
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Thomas Cleff: Pforzheim University of Applied Sciences

Chapter Chapter 13 in Applied Statistics and Multivariate Data Analysis for Business and Economics, 2025, pp 471-499 from Springer

Abstract: Abstract This chapter explores two primary methods of cluster analysis: hierarchical and k-means clustering. Hierarchical clustering involves the creation of a nested sequence of clusters by either merging or splitting them, whereas k-means clustering requires the number of clusters to be predetermined and optimizes the assignment of individual observations to a cluster. The chapter discusses key concepts such as proximity measures and methods for determining the optimal number of clusters. Practical guidance is given on how to perform cluster analysis using R, SPSS, and Stata. Strategies for evaluating the quality of the cluster solution are also provided.

Keywords: Cluster analysis; Hierarchical clustering; K-means clustering; Proximity measures; Distance measures; Linkage methods; Ward method; Dendrogram (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-78070-7_13

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