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Cluster Analysis

Part 2. Marketing Research

  1. Michel Wedel,
  2. Wei Shi (Savannah)

Published Online: 15 DEC 2010

DOI: 10.1002/9781444316568.wiem02018

Wiley International Encyclopedia of Marketing

Wiley International Encyclopedia of Marketing

How to Cite

Wedel, M. and Shi (Savannah), W. 2010. Cluster Analysis. Wiley International Encyclopedia of Marketing. 2.

Author Information

  1. University of Maryland, College Park, MD, USA

Publication History

  1. Published Online: 15 DEC 2010

Abstract

Cluster analysis is used to categorize consumers into clusters that are homogeneous along a range of variables. In marketing, it is most often applied for purposes of market segmentation, product perceptual mapping, and data mining. We discuss two important clustering methods here: hierarchical clustering as a “bottom-up” procedure, and nonhierarchical clustering as a “top-down” procedure. Hierarchical clustering begins with each consumer in a cluster by itself. Then, with a (dis)similarity metric, subjects that are similar are taken into the same cluster. We provide an introduction to popular (dis)similarity metrics for continuous and discrete variables, as well as main hierarchical clustering algorithms. The result of hierarchical classification is a dendrogram: a tree structure that represents the hierarchical relations among all subjects being clustered. The nonhierarchical clustering methods, instead, partition the data into a predetermined number of segments and try to minimize some criterion of interest. K-means clustering, partitioning around mediods (PAM), and fuzzy clustering are some of the most popular nonhierarchical algorithms. We also touch upon the issue regarding performance of different clustering algorithms, decision on the number of clusters, clustering validation, and software available for the clustering algorithms.

Keywords:

  • cluster analysis;
  • hierarchical methods;
  • dendrogram;
  • nonhierarchical methods;
  • similarity matrix;
  • market segmentation