CIRCUIT CLUSTERING USING A STOCHASTIC FLOW-INJECTION METHOD

We present a new clustering metric, based on a random graph model and a ratio cut concept. The minimization of the proposed clustering cost can be transformed to a uniform multicommodity flow problem by adding artificial weight functions, which can be solved by a multicommodity f low-based algorithm with high complexity. We devise a probabilistic fl ow injection approach which drastically reduces the complexity of the flow-based algorithm. Experimental results show that this algorithm ge nerates promising results with respect to the proposed metric.