Clustering Documents And Gaussian Data With Dirichlet Process Mixture Models

This article is the fifth part of the tutorial on Clustering with DPMM. In the previous posts we covered in detail the theoretical background of the method and we described its mathematical representationsmu and ways to construct it. In this post we will try to link the theory with the practice by introducing two models DPMM: the Dirichlet Multivariate Normal Mixture Model which can be used to cluster Gaussian data and the Dirichlet-Multinomial Mixture Model which is used to cluster documents. Update: The Datumbox Machine Learning Framework is now open-source and free to download. Check out the package com.datumbox.framework.machinelearning.clustering to see the implementation of Dirichlet Process Mixture Models in Java.