Hierarchical cluster analysis (or hierarchical clustering) is a general approach to cluster analysis , in which the object is to group together objects or records that are "close" to one another. A key component of the analysis is repeated calculation of distance measures between objects, and between clusters once objects begin to be grouped into clusters. The outcome is represented graphically as a dendrogram .

The initial data for the hierarchical cluster analysis of N objects is a set of object-to-object distances and a linkage function for computation of the cluster-to-cluster distances.

The two main categories of methods for hierarchical cluster analysis are divisive methods and agglomerative methods . In practice, the agglomerative methods are of wider use. On each step, the pair of clusters with smallest cluster-to-cluster distance is fused into a single cluster. The most common algorithms for hierarchical clustering are:

• Single linkage clustering ;

• Complete linkage clustering ;

• Average linkage clustering ;

• Average group linkage ;

• Ward's linkage .

These algorithms differ mainly by the linkage function - the method for calculation of cluster-to-cluster distance.

See also: the chapter in XLMiner help , and the short online courses

Cluster Analysis ,

Data Mining: Unsupervised Techniques ,

Introduction to Data Mining .