Kernel Conditional Clustering.

Xiao He, Thomas Gumbsch, Damian Roqueiro, Karsten M. Borgwardt

Abstract: Clustering results are often affected by covariates that are independent of the clusters one would like to discover. Traditionally, Alternative Clustering algorithms can be used to solve such a problem. However, these suffer from at least one of the following problems: i) continuous covariates or non-linearly separable clusters cannot be handled; ii) assumptions are made about the distribution of the data; iii) one or more hyper-parameters need to be set. Here we propose a novel algorithm, named Kernel Conditional Clustering (KCC), whose objective is derived from a kernel based conditional dependence measure. KCC is parameter-light and makes no assumptions about the cluster structure, the covariates, or the distribution of the data. On both simulated and real-world datasets, the proposed KCC algorithm detects the ground truth cluster structures more accurately than state-of-the-art alternative clustering methods.