  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.
