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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.