Cluster Analysis

Marko Sarstedt and Erik Mooi
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Marko Sarstedt: Otto-von-Guericke-Universität
Erik Mooi: University of Melbourne

Chapter 9 in A Concise Guide to Market Research, 2014, pp 273-324 from Springer

Abstract: Learning Objectives After reading this chapter you should understand: The basic concepts of cluster analysis. How basic cluster algorithms work. How to compute simple clustering results manually. The different types of clustering procedures. The SPSS clustering outputs.

Keywords: Agglomerative and divisive clustering; Chebychev distance; City-block distance; Clustering variables; Dendrogram; Distance matrix; Euclidean distance; Hierarchical and partitioning methods; Icicle diagram; k-means; Matching coefficients; Profiling clusters; Two-step clustering (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/978-3-642-53965-7_9

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