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.