Information Theoretic Clustering using Kernel Density Estimation

In recent years, information-theoretic clustering algorithms have been proposed which assign data points to clusters so as to maximize the mutual information between cluster labels and data [1, 2]. Using mutual information for clustering has several attractive properties: it is flexible enough to fit complex patterns in the data, and allows for a principled approach to clustering without assuming an explicit probabilistic generative model for the data.