High-utility itemset mining is the task of finding the sets of items that yield a high utility (e.g. profit) in quantitative transaction databases. An important limitation of previous work on high-utility itemset mining is that utility is generally used as the sole criterion for assessing the interestingness of patterns. This leads to finding many itemsets that have a high profit but contain items that are weakly correlated. To address this issue, this paper proposes to integrate the concept of correlation in high-utility itemset mining to find profitable itemsets that are highly correlated, using the bond measure. An efficient algorithm named FCHM (Fast Correlated high-utility itemset Miner) is proposed to efficiently discover correlated high-utility itemsets. Experimental results show that FCHM is highly-efficient and can prune a huge amount of weakly correlated HUIs.