Ensemble-driven support vector clustering: From ensemble learning to automatic parameter estimation

TL;DR

This paper introduces EDSVC, an unsupervised ensemble-based method that automatically estimates key parameters for support vector clustering, enabling robust clustering of arbitrary shapes without supervision.

Contribution

It presents the first algorithm that automatically estimates SVC parameters using ensemble learning, improving its practical applicability.

Findings

Effective parameter estimation on real-world datasets

Robust clustering results achieved without supervision

Outperforms existing methods in accuracy and stability

Abstract

Support vector clustering (SVC) is a versatile clustering technique that is able to identify clusters of arbitrary shapes by exploiting the kernel trick. However, one hurdle that restricts the application of SVC lies in its sensitivity to the kernel parameter and the trade-off parameter. Although many extensions of SVC have been developed, to the best of our knowledge, there is still no algorithm that is able to effectively estimate the two crucial parameters in SVC without supervision. In this paper, we propose a novel support vector clustering approach termed ensemble-driven support vector clustering (EDSVC), which for the first time tackles the automatic parameter estimation problem for SVC based on ensemble learning, and is capable of producing robust clustering results in a purely unsupervised manner. Experimental results on multiple real-world datasets demonstrate the effectiveness of our approach.