2014 CorrelationClusteringinMapReduc

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Correlation clustering is a basic primitive in data miner's toolkit with applications ranging from entity matching to social network analysis. The goal in correlation clustering is, given a graph with signed edges, partition the nodes into clusters to minimize the number of disagreements. In this paper we obtain a new algorithm for correlation clustering. Our algorithm is easily implementable in computational models such as MapReduce and streaming, and runs in a small number of rounds. In addition, we show that our algorithm obtains an almost 3-approximation to the optimal correlation clustering. Experiments on huge graphs demonstrate the scalability of our algorithm and its applicability to data mining problems.



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2014 CorrelationClusteringinMapReducFlavio Chierichetti
Nilesh Dalvi
Ravi Kumar
Correlation Clustering in MapReduce10.1145/2623330.26237432014