  Traditional plane-based clustering methods measure the cost of within-cluster
and between-cluster by quadratic, linear or some other unbounded functions,
which may amplify the impact of cost. This letter introduces a ramp cost
function into the plane-based clustering to propose a new clustering method,
called ramp-based twin support vector clustering (RampTWSVC). RampTWSVC is more
robust because of its boundness, and thus it is more easier to find the
intrinsic clusters than other plane-based clustering methods. The non-convex
programming problem in RampTWSVC is solved efficiently through an alternating
iteration algorithm, and its local solution can be obtained in a finite number
of iterations theoretically. In addition, the nonlinear manifold-based
formation of RampTWSVC is also proposed by kernel trick. Experimental results
on several benchmark datasets show the better performance of our RampTWSVC
compared with other plane-based clustering methods.
