  Mining useful patterns from varied types of databases is an important
research topic, which has many real-life applications. Most studies have
considered the frequency as sole interestingness measure for identifying high
quality patterns. However, each object is different in nature. The relative
importance of objects is not equal, in terms of criteria such as the utility,
risk, or interest. Besides, another limitation of frequent patterns is that
they generally have a low occupancy, i.e., they often represent small sets of
items in transactions containing many items, and thus may not be truly
representative of these transactions. To extract high quality patterns in real
life applications, this paper extends the occupancy measure to also assess the
utility of patterns in transaction databases. We propose an efficient algorithm
named High Utility Occupancy Pattern Mining (HUOPM). It considers user
preferences in terms of frequency, utility, and occupancy. A novel
Frequency-Utility tree (FU-tree) and two compact data structures, called the
utility-occupancy list and FU-table, are designed to provide global and partial
downward closure properties for pruning the search space. The proposed method
can efficiently discover the complete set of high quality patterns without
candidate generation. Extensive experiments have been conducted on several
datasets to evaluate the effectiveness and efficiency of the proposed
algorithm. Results show that the derived patterns are intelligible, reasonable
and acceptable, and that HUOPM with its pruning strategies outperforms the
state-of-the-art algorithm, in terms of runtime and search space, respectively.
