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Lecture Notes in Computer Science
Correlation Clustering is an elegant model that captures fundamental graph cut problems such as Min s − t Cut, Multiway Cut, and Multicut, extensively studied in combinatorial optimization. Here, we are given a graph with edges labeled + or − and the goal is to produce a clustering that agrees with the labels as much as possible: + edges within clusters and − edges across clusters. The classical approach towards Correlation Clustering (and other graph cut problems) is to optimize a globaldoi:10.1007/978-3-319-59250-3_12 fatcat:ojmqzw75ujf3lk4xl4jc2o5rym