Cluster analysis is the statistical method of partitioning a sample into homogeneous classes to produce an operational classification.  Such a classification may help:

• formulate hypotheses concerning the origin of the sample, e.g. in evolution studies
• describe a sample in terms of a typology, e.g. for market analysis or administrative purposes
• predict the future behaviour of population types, e.g. in modelling economic prospects for different industry sectors
• optimize functional processes, e.g. business site locations or product design
• assist in identification, e.g. in diagnosing diseases
• measure the different effects of treatments on classes within the population, e.g. with analysis of variance

To see some applications of cluster analysis which illustrate the diversity of the subject select Clustering Examples. In each study, either a grouping of the sample into a relatively small number of clusters was obtained, or the similarity relationships between the cases was analyzed and described.

Several Case Studies are supplied with ClustanGraphics, and are documented in the Help file. Some are used to illustrate its features throughout this website, and in the ClustanGraphics Primer .

Remember that at Clustan we have been working in this field since the 1960s.  Ask for our training and consultancy services , if you would like to benefit from our wide ranging and highly specialized expertise.