Distributed Kernel K-Means for Large Scale Clustering

TL;DR

This paper introduces a scalable, approximate kernel k-means clustering method optimized for large datasets, leveraging parallel CPU-GPU architectures to balance accuracy and computational efficiency.

Contribution

It proposes a novel approximation and parallelization scheme for kernel k-means, enabling effective large-scale clustering with automatic memory-based accuracy control.

Findings

Effective on UCI datasets

Successful application to molecular dynamics data

Balances accuracy and speed automatically

Abstract

Clustering samples according to an effective metric and/or vector space representation is a challenging unsupervised learning task with a wide spectrum of applications. Among several clustering algorithms, k-means and its kernelized version have still a wide audience because of their conceptual simplicity and efficacy. However, the systematic application of the kernelized version of k-means is hampered by its inherent square scaling in memory with the number of samples. In this contribution, we devise an approximate strategy to minimize the kernel k-means cost function in which the trade-off between accuracy and velocity is automatically ruled by the available system memory. Moreover, we define an ad-hoc parallelization scheme well suited for hybrid cpu-gpu state-of-the-art parallel architectures. We proved the effectiveness both of the approximation scheme and of the parallelization method on standard UCI datasets and on molecular dynamics (MD) data in the realm of computational chemistry. In this applicative domain, clustering can play a key role for both quantitively estimating kinetics rates via Markov State Models or to give qualitatively a human compatible summarization of the underlying chemical phenomenon under study. For these reasons, we selected it as a valuable real-world application scenario.