Hierarchical cluster analysis begins by separating each object into a cluster by itself. At each stage of the analysis, the criterion by which objects are separated is relaxed in order to link the two most similar clusters until all of the objects are joined in a complete classification tree.

The basic criterion for any clustering is distance. Objects that are near each other should belong to the same cluster, and objects that are far from each other should belong to different clusters. For a given set of data, the clusters that are constructed depend on your specification of the following parameters:

- Cluster method defines the rules for cluster formation. For example, when calculating the distance between two clusters, you can use the pair of nearest objects between clusters or the pair of furthest objects, or a compromise between these methods.
- Measure defines the formula for calculating distance. For example, the Euclidean distance measure calculates the distance as a "straight line" between two clusters. Interval measures assume that the variables are scale; count measures assume that they are discrete numeric; and binary measures assume that they take only two values.
- Standardization allows you to equalize the effect of variables measured on different scales.