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Cluster Analysis

An analytical technique that arranges research data into mutually exclusive and collectively exhaustive groups (or clusters) where the contents of each cluster are similar to each other, but different to the other clusters in the analysis.

What is a cluster analysis in statistics?

Cluster analysis is a multivariate method that aims to classify a sample of subjects (or ob- jects) on the basis of a set of measured variables into a number of different groups such that similar subjects are placed in the same group.

What is the purpose of cluster analysis?

Cluster analysis is a class of techniques that are used to classify objects or cases into relative groups called clusters. Cluster Analysis has been used in marketing for various purposes. Segmentation of consumers in cluster analysis is used on the basis of benefits sought from the purchase of the product.

What is a cluster analysis example?

The term cluster analysis (first used by Tryon, 1939) encompasses a number of different algorithms and methods for grouping objects of similar kind into respective categories. For example, a group of diners sharing the same table in a restaurant may be regarded as a cluster of people.

How does cluster analysis work?

Cluster analysis is an exploratory analysis that tries to identify structures within the data. Cluster analysis is also called segmentation analysis or taxonomy analysis. More specifically, it tries to identify homogenous groups of cases if the grouping is not previously known.

What is Cluster analysis in marketing research?

The purpose of cluster analysis is to classify objects or people into several mutually exclusive and exhaustive groups in such a way that those included in a group are as similar as possible to each other. Read the full article for more details.

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Cluster Analysis

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