Cluster Analysis

We provide comprehensive and advanced knowledge of cluster analysis knowledge. We first introduce the principles of cluster analysis and outline the steps and decisions involved. We discuss how to select appropriate clustering variables and subsequently introduce modern hierarchical and partitioning methods for cluster analysis, using simple examples to illustrate how they work. We also discuss the key measures of similarity and dissimilarity, and offer guidance on how to decide the number of clusters to extract from the data. Each step in a cluster analysis is subsequently linked to its execution in SPSS, thus enabling readers to analyze, chart, and validate the results. Interpretation of SPSS output can be difficult, but we make this easier by means of an annotated case study. We conclude with suggestions for further readings on the use, application, and interpretation of cluster analysis.

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Notes

Tonks (2009) provides a discussion of segment design and the choice of clustering variables in consumer markets.

See Arabie and Hubert (1994), Sheppard (1996), and Dolnicar and Grün (2009).

Whereas agglomerative methods have the large task of checking N·(N–1)/2 possible first combinations of observations (note that N represents the number of observations in the dataset), divisive methods have the almost impossible task of checking 2 ( N -1) –1 combinations.

There are many other matching coefficients, with exotic names such as Yule’s Q , Kulczynski , or Ochiai , which are also menu-accessible in SPSS. As most applications of cluster analysis rely on metric or ordinal data, we will not discuss these. See Wedel and Kamakura (2000) for more information on alternative matching coefficients.

See Punji and Stewart (1983) for additional information on this sequential approach.

The strong emphasis of gender in determining the solution supports prior research, which found that two-step clustering puts greater emphasis on categorical variables in the results computation (Bacher et al. 2004).

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Authors and Affiliations

Faculty of Economics and Management, Otto-von-Guericke- University Magdeburg, Magdeburg, Germany Marko Sarstedt
Department of Management and Marketing, The University of Melbourne, Parkville, VIC, Australia Erik Mooi