Abstract
A cluster analysis is used to identify groups of objects that are “similar”. This chapter explains the general procedure for determining clusters of similar objects. To do so, measures of similarity or dissimilarity are outlined.
After finishing this chapter, the reader is able to:
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1.
Evaluate data using more complex statistical techniques such as cluster analysis,
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2.
Explain the difference between several approaches to deal with large datasets in cluster analysis by using TwoStep or K-Means algorithm,
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Describe the advantages and the pitfalls of the cluster analysis methods,
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4.
Apply TwoStep or K-Means and explain the results as well as
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5.
Describe the usage of the Auto Clustering node of the IBM SPSS Modeler and its pitfalls.
Ultimately, the reader will be called upon to propose well-thought-out and practical business actions from the statistical results.
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Wendler, T., Gröttrup, S. (2021). Cluster Analysis. In: Data Mining with SPSS Modeler. Springer, Cham. https://doi.org/10.1007/978-3-030-54338-9_7
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DOI: https://doi.org/10.1007/978-3-030-54338-9_7
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