Frequent itemsetmining and high-utility itemsetmining have been widely
applied to the extraction of useful information from databases. However,
with the proliferation of the Internet of Things, smart devices are
generating vast amounts of data daily, and studies focusing on individual
dimensions are increasingly unable to support decision-making. Hence, the
concept of a skyline query considering frequency and utility (which returns
a set of points that are not dominated by other points) was introduced.
However, in most cases, firms are concerned about not only the frequency of
purchases but also quantities. The skyline quantity-utility pattern (SQUP)
considers both the quantity and utility of items. This paper proposes two
algorithms, FSKYQUP-Miner and FSKYQUP, to efficiently mine SQUPs. The
algorithms are based on the utility-quantity list structure and include an
effective pruning strategy which calculates the minimum utility of SQUPs
after one scan of the database and prunes undesired items in advance, which
greatly reduces the number of concatenation operations. Furthermore, this
paper proposes an array structure superior to utilmax for storing the
maximum utility of quantities, which further improves the efficiency of
pruning. Extensive comparison experiments on different datasets show that
the proposed algorithms find all SQUPs accurately and efficiently.