  We study Frank-Wolfe algorithms - standard, pairwise, and away-steps - for
efficient optimization of Dominant Set Clustering. We present a unified and
computationally efficient framework to employ the different variants of
Frank-Wolfe methods, and we investigate its effectiveness via several
experimental studies. In addition, we provide explicit convergence rates for
the algorithms in terms of the so-called Frank-Wolfe gap. The theoretical
analysis has been specialized to Dominant Set Clustering and covers
consistently the different variants.
