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Erschienen in:

2018 | OriginalPaper | Buchkapitel

9. Cluster Analysis

verfasst von : Erik Mooi, Marko Sarstedt, Irma Mooi-Reci

Erschienen in: Market Research

Verlag: Springer Singapore

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Abstract

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 Stata (using menus and code), thus enabling readers to analyze, chart, and validate the results. Interpretation of Stata 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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Vorheriges Kapitel Principal Component and Factor Analysis

Nächstes Kapitel Communicating the Results

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, such as Yule’s Q, Kulczynski, or Ochiai, which are also menu-accessible in Stata. However, since 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.

For details on the implementation of these stopping rules in Stata, see Halpin (2016).

In the

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Web Appendix (→Downloads), we offer a Stata.ado file to calculate the ω _k called chomega.ado. We also offer an Excel sheet (VRC.xlsx) to calculate the ω _k manually.

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

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Titel: Cluster Analysis
verfasst von: Erik Mooi
Marko Sarstedt
Irma Mooi-Reci
Verlag: Springer Singapore
Buch: Market Research
Print ISBN: 978-981-10-5217-0

Electronic ISBN: 978-981-10-5218-7

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-981-10-5218-7_9