  As an important data mining technology, high utility itemset mining (HUIM) is
used to find out interesting but hidden information (e.g., profit and risk).
HUIM has been widely applied in many application scenarios, such as market
analysis, medical detection, and web click stream analysis. However, most
previous HUIM approaches often ignore the relationship between items in an
itemset. Therefore, many irrelevant combinations (e.g., \{gold, apple\} and
\{notebook, book\}) are discovered in HUIM. To address this limitation, many
algorithms have been proposed to mine correlated high utility itemsets
(CoHUIs). In this paper, we propose a novel algorithm called the Itemset
Utility Maximization with Correlation Measure (CoIUM), which considers both a
strong correlation and the profitable values of the items. Besides, the novel
algorithm adopts a database projection mechanism to reduce the cost of database
scanning. Moreover, two upper bounds and four pruning strategies are utilized
to effectively prune the search space. And a concise array-based structure
named utility-bin is used to calculate and store the adopted upper bounds in
linear time and space. Finally, extensive experimental results on dense and
sparse datasets demonstrate that CoIUM significantly outperforms the
state-of-the-art algorithms in terms of runtime and memory consumption.
