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Cluster Analysis

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A Concise Guide to Market Research

Part of the book series: Springer Texts in Business and Economics ((STBE))

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.

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Notes

  1. 1.

    See Wedel and Kamakura (2000).

  2. 2.

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

  3. 3.

    See the studies by Arabie and Hubert (1994), Sheppard (1996), or Dolnicar and Grün (2009).

  4. 4.

    See Wedel and Kamakura (2000), Dolnicar (2003), and Kaufman and Rousseeuw (2005) for a review of clustering techniques.

  5. 5.

    Note that researchers also often use the squared Euclidean distance.

  6. 6.

    See Milligan and Cooper (1988).

  7. 7.

    There are many other matching coefficients such as Yule’s Q, Kulczynski or Ochiai, but since most applications of cluster analysis rely on metric or ordinal data, we will not discuss these in greater detail. Check Wedel and Kamakura (2000) for more information on alternative matching coefficients.

  8. 8.

    Note that because of ties, the final results may depend on the order of objects in the input file. Against this background, van der Kloot et al. (2005) recommend re-running the analysis with different input order of the data. At the same time, however, ties are more the exception than the rule in practical applications and generally don't have a pronounced impact on the results.

  9. 9.

    Milligan and Cooper (1985) compare various criteria.

  10. 10.

    Note that the k-means algorithm is one of the simplest non-hierarchical clustering methods. Several extensions, such as k-medoids (Kaufman and Rousseeuw 2005) have been proposed to handle limitations of the procedure. More advanced methods include finite mixture models (McLachlan and Peel 2000), neural networks (Bishop 2006), and self-organizing maps (Kohonen 1982). Andrews and Currim (2003) discuss the validity of some of these approaches.

  11. 11.

    Conversely, SPSS always sets one observation as the cluster center instead of picking some random point in the dataset.

  12. 12.

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

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Author information

Authors and Affiliations

  1. Faculty of Economics and Management, Otto-von-Guericke-Universität, Magdeburg, Germany

    Marko Sarstedt

  2. Faculty of Business and Law, University of Newcastle, Callaghan, Australia

    Marko Sarstedt

  3. Faculty of Business and Economics, University of Melbourne, Parkville, Victoria, Australia

    Erik Mooi

  4. Aston Business School, University of Aston, Birmingham, UK

    Erik Mooi

Authors
  1. Marko Sarstedt
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  2. Erik Mooi
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Sarstedt, M., Mooi, E. (2014). Cluster Analysis. In: A Concise Guide to Market Research. Springer Texts in Business and Economics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53965-7_9

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