Ensemble Clustering via Random Walker Consensus Strategy

In this paper we present the adaptation of a random walker algorithm for combination of image segmentations to work with clustering problems. In order to achieve it, we pre-process the ensemble of clusterings to generate its graph representation. We show experimentally that a very small neighborhood will produce similar results if compared with larger choices. This fact alone improves the computational time needed to produce the final consensual clustering. We also present an experimental comparison between our results against other graph based and well known combination clustering methods in order to assess the quality of this approach.