  Utility-driven itemset mining is widely applied in many real-world scenarios.
However, most algorithms do not work for itemsets with negative utilities.
Several efficient algorithms for high-utility itemset (HUI) mining with
negative utilities have been proposed. These algorithms can find complete HUIs
with or without negative utilities. However, the major problem with these
algorithms is how to select an appropriate minimum utility (minUtil) threshold.
To address this issue, some efficient algorithms for extracting top-k HUIs have
been proposed, where parameter k is the quantity of HUIs to be discovered.
However, all of these algorithms can solve only one part of the above problem.
In this paper, we present a method for TOP-k high-utility Itemset disCovering
(TOPIC) with positive and negative utility values, which utilizes the
advantages of the above algorithms. TOPIC adopts transaction merging and
database projection techniques to reduce the database scanning cost, and
utilizes minUtil threshold raising strategies. It also uses an array-based
utility technique, which calculates the utility of itemsets and upper bounds in
linear time. We conducted extensive experiments on several real and synthetic
datasets, and the results showed that TOPIC outperforms state-of-the-art
algorithm in terms of runtime, memory costs, and scalability.
