We present a message-passing based parallel algorithm for mining Correlated Heavy Hitters from a two-dimensional data stream. To the best of our knowledge, this is the first parallel algorithm solving the problem. We show, through experimental results, that our algorithm provides very good scalability, whilst retaining the accuracy of its sequential counterpart.
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