Introduction to Hierarchical Clustering
Until this point, we have shown that hierarchies can be excellent structures in which to organize information that clearly show nested relationships among data points. While this is helpful in gaining an understanding of the parent/child relationships between items, it can also be very handy when forming clusters. Expanding on the animal example of the prior 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 the same structure in order to identify which records in your dataset correspond to dogs or cats, as well as their relative subspecies.
Since you are only given animal heights and weights, you won't be able to extrapolate the specific names of each species. However, by analyzing the features that you have been provided, you can develop a structure within the data that serves as an...
