Before we dive into hierarchical clustering, it would be a very handy exercise to go through the scikit-learn documentation on clustering [8]. We have to remember that using a different model in scikit-learn is very easy, and that almost all the other steps in the process of clustering remain the same throughout.
We will use Ward's algorithm/method [9] to attempt hierarchical clustering. The algorithm is based on the idea of reducing the variance within each cluster and uses distance measures to do this. Ward's method is one of the earliest methods used in various hierarchical clustering algorithms, which are based on building clusters and arranging them in a hierarchy. In our examples, we will use dendrograms [10] to represent our hierarchical clusters.
To set up our dataset for this method we must first create a matrix with pair-wise distances...
