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Cluster Analysis

  • Cluster analysis is a statistical procedure that attempts to classify a group into like or ‘homogeneous’ sub-groups. It is usually used as a segmentation tool where people are grouped together into segments based on their attitudes, behaviors, demographics, or some combination of these. However cluster analysis can also be used to cluster variables (instead of cases) into like groups as well. The task is very analogous to a coder developing a code list in that individual responses are read and classified into groups that capture the common meaning.
  • Cluster analysis is often considered to be more of an art than a science. Of all the common statistical procedures, cluster analysis gives the least statistical guidance as to whether the solution it generates is meaningful or not. The cluster analysis algorithm does not tell the researcher the ‘correct’ number of clusters in a data set. Instead, the researcher has to produce and examine a number of different cluster solutions and decide which solution is the best. So the analyst may generate cluster solutions for two clusters, three, four and so on up to 10 or more clusters. Between different clustering algorithms, number of clusters produced, and options for how the data is processed, a considerable number of cluster solutions can be generated.
  • To evaluate the solutions, the researcher generally compares the individual groups (i.e., start by comparing the groups in the two cluster solution, then compare the groups in the three cluster solution and so on) for each solution on a series of demographic, attitudinal or other measures. Other statistical procedures can be used in the evaluation process, but often times the analyst tried to interpret each solution by how it fits with the other variables, and chooses the solution that seems to fit the best.
  • Kohonen Self Organizing Maps (SOMs) are a form of Neural Network (an Artificial Intelligence technology) that also ‘clusters’ cases into like groups using a different mathematical approach. To the researcher, SOMs do just what a K-Means cluster program does, but in a different way. However, if a SOM and K-Means cluster program are told programmed to produce the same number of cluster groups, the cases will be assigned somewhat differently. Often times the SOM solution will be superior.
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