Hierarchical Cluster Analysis:
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.