With the increasing prevalence of richer, more complex data sources, learning with multiple views is becoming more widespread. Multiple kernel learning (MKL) has been developed to address this problem, but in general, the solutions provided by traditional MKL are restricted to a classification objective function. In this work, we develop a novel multiple kernel learning algorithm that is based on a spectral clustering objective function which is able to find an optimal kernel weight vector for the clustering problem. We go on to show how this optimization problem can be cast as a semidefinite program and efficiently solved using off-the-shelf interior point methods.