In this chapter, you learned about model-based clustering, the Dirichlet process, and topic modeling. In model-based clustering, we try to get model from the data, while the Dirichlet process is used to understand the data. Topic modeling helps us to identify the topics in an article or in a set of documents. We discussed how Mahout has implemented topic modeling using the latent Dirichlet process and how it is implemented in map reduce.
We also discussed how to use Mahout to find the topic of distribution on the set of documents. In the next chapter, we will discuss one more new algorithm—Streaming K-means.