Disjoint clustering is more efficient for large data sets. A second important decision is how to determine which items are similar to each other. Many different ways of measuring similarity have been proposed, and it is important to choose one that is appropriate for your data. Third, a criterion for adding items and to clusters and combining clusters must be chosen. For example, an item may be added to a cluster if it is similar to one item in that cluster, or if it is similar to the average item in the cluster.
Different criteria can result in very different classifications of items.
Finally, the number of clusters must be chosen. Cluster analysis can be performed by most common statistical software packages, including SAS, SPSS, and Minitab. If you would like to discuss using cluster analysis, contact the Office of Statistical Consulting. Please forward it to any interested colleagues and research staff. Anyone not receiving this newsletter who would like to be added to the mailing list for future newsletters should contact Karen Grace-Martin .