Kernel Conditional Clustering

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 nonlinearly 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.