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In correlation clustering, the input is a graph with edge-weights, where every edge is labelled either + or − according to similarity of its endpoints. The goal is to produce a partition of the vertices that disagrees with the edge labels as little as possible. In two-edge-connected augmentation, the input is a graph with edge-weights and a subset R of edges of the graph. The goal is to produce a minimum weight subset S of edges of the graph, such that for every edge in R, its endpoints aredoi:10.4230/lipics.stacs.2015.554 dblp:conf/stacs/KleinMZ15 fatcat:7h7w2543qbdbxixjf25ijziacq