Improved Algorithms for the Random Cluster Graph Model
We model noisy clustering data using random graphs: Clusters correspond to disjoint sets of vertices. Two vertices from the same set (resp., different sets) share an edge with probability p (resp., r<p ). We give algorithms that reconstruct the clusters from the graph with high probability. Compared to previous studies, our algorithms have lower time complexity and apply under wider parameter range.