Heirarchical Clustering Techniques using R

The idea behind hierarchical cluster analysis is to show which of a (potentially large) set of samples are most similar to one another, and to group these similar samples in the same limb of a tree. Each of the samples can be thought of a sitting in an m-dimensional space, defined by the m variables (columns) in the dataframe. We define similarity on the basis of the distance between two samples in this m-dimensional space. Several different distance measures could be used, but the default is Euclidean distance and this is used to work out the distance from every sample to every other sample. This quantitative dissimilarity structure of the data is stored in a matrix produced by the "dist function".