Finally, we have our vectors that we believe capture the posts to a sufficient degree. Not surprisingly, there are many ways to group them together. Most clustering algorithms fall into one of the two methods, flat and hierarchical clustering.
Flat clustering divides the posts into a set of clusters without relating the clusters to each other. The goal is simply to come up with a partitioning such that all posts in one cluster are most similar to each other while being dissimilar from the posts in all other clusters. Many flat clustering algorithms require the number of clusters to be specified up front.
In hierarchical clustering, the number of clusters does not have to be specified. Instead, the hierarchical clustering creates a hierarchy of clusters. While similar posts are grouped into one cluster, similar clusters are again grouped into one uber-cluster. This is done recursively, until only one cluster is left, which contains everything. In this hierarchy, one can then choose...