Agglomerative versus Divisive Clustering
Our instances of hierarchical clustering so far have all been agglomerative – that is, they have been built from the bottom up. While this is typically the most common approach for this type of clustering, it is important to know that it is not the only way a hierarchy can be created. The opposite hierarchical approach, that is, built from the top up, can also be used to create your taxonomy. This approach is called Divisive Hierarchical Clustering and works by having all the data points in your dataset in one massive cluster. Many of the internal mechanics of the divisive approach will prove to be quite similar to the agglomerative approach:
Figure 2.15: Agglomerative versus divisive hierarchical clustering
As with most problems in unsupervised learning, deciding the best approach is often highly dependent on the problem you are faced with solving.
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