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Cluster analysis is a class of statistical techniques that can be applied to data that exhibit “natural” groupings. Cluster analysis sorts through the raw data and groups them into clusters, where a cluster is a group of relatively homogeneous cases or observations. Objects in a cluster are similar to each other. They are also dissimilar to objects outside the cluster, particularly objects in other clusters.
Whereas factor analysis reduces the number of variables by grouping them into a smaller set of factors, cluster analysis reduces the number of observations or cases by grouping them into a smaller set of clusters.
Cluster analysis is widely used when working with multivariate data from surveys and test panels. Market researchers use cluster analysis to partition the general population of consumers into market segments and to better understand the relationships between different groups of consumers/potential customers, and for use in:
- Market segmentation
- Product positioning
- New product development
The actual meaning of the clusters is up to the analyst to interpret, based on the variables included in the cluster.
Note: Loop questions cannot be used in cluster analysis.
Constraints
Cluster Analysis calculations can only be performed based on variables which contain a numeric measurement. Thus only the following questions can be used: Numeric, Numeric list answers, Singles with all numerical codes/scale, and Grid answers with all numerical codes/scale. Note that Grid and Numeric List questions can be dropped onto the variables field, but will be expanded to the corresponding grid/list answer questions.
Only filters of type Filter Expression are supported in Cluster Analysis calculations.