In the field of data mining, many utility-oriented mining approaches have been extensively studied. Previous studies have, however, the limitation that they rarely consider the inherent correlation of items among the discovered patterns. For example, from the purchase behavior, a high-utility group of products (w.r.t. multi-products) may contain the items with both high or low utility. This pattern is also considered as a valuable pattern even if they may not be highly correlated, or even happened together by the chance. In this paper, we propose an efficient utility mining approach namely non-redundant Correlated high-Utility Pattern Miner (CoUPM) by considering both strong positive correlation and profitable value of the products. The derived patterns with high utility and strong correlation can lead to more insightful availability than those patterns only have high utility values. The utility-list structure is maintained and applied to store necessary information of correlation and utility. Several pruning strategies are further developed to improve the efficiency for discovering the desired patterns. Experimental results show that the non-redundant correlated high-utility patterns have more effectiveness than some other kinds of patterns. Moreover, the proposed CoUPM algorithm significantly outperforms the state-of-the-art algorithm.