  The problem of mining Correlated Heavy Hitters (CHH) from a two-dimensional
data stream has been introduced recently, and a deterministic algorithm based
on the use of the Misra--Gries algorithm has been proposed by Lahiri et al. to
solve it. In this paper we present a new counter-based algorithm for tracking
CHHs, formally prove its error bounds and correctness and show, through
extensive experimental results, that our algorithm outperforms the Misra--Gries
based algorithm with regard to accuracy and speed whilst requiring
asymptotically much less space.
