Introduction to Hierarchical Clustering

So far, we have shown you that hierarchies can be excellent structures to organize information that clearly shows nested relationships among data points. While this helps us gain an understanding of the parent/child relationships between items, it can also be very handy when forming clusters. Expanding on the animal example in the previous section, imagine that you were simply presented with two features of animals: their height (measured from the tip of the nose to the end of the tail) and their weight. Using this information, you then have to recreate a hierarchical structure in order to identify which records in your dataset correspond to dogs and cats, as well as their relative subspecies.

Since you are only given animal heights and weights, you won't be able to deduce the specific names of each species. However, by analyzing the features that you have been provided with, you can develop a structure within the data that serves as...