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9. Cluster Analysis

  • 2018
  • OriginalPaper
  • Chapter
Published in:
Authors
Erik Mooi
Marko Sarstedt
Irma Mooi-Reci
Published in
Market Research
Publisher
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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previous chapter Principal Component and Factor Analysis
next chapter Communicating the Results
Title
Cluster Analysis
Authors
Erik Mooi
Marko Sarstedt
Irma Mooi-Reci
Copyright Year
2018
Publisher
Springer Singapore
Book
Market Research

Print ISBN: 978-981-10-5217-0

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

Copyright Year: 2018

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