The main purpose of data mining and analytics is to find novel, potentially useful patterns
that can be utilized in real-world applications to derive beneficial knowledge. For identifying …

Traditional association rule mining has been widely studied. But it is unsuitable for real‐
world applications where factors such as unit profits of items and purchase quantities must …

High utility itemset mining is a challenging task in frequent pattern mining, which has wide
applications. The state-of-the-art algorithm is HUI-Miner. It adopts a vertical representation …

High utility itemsets refer to the sets of items with high utility like profit in a database, and
efficient mining of high utility itemsets plays a crucial role in many real-life applications and …

In recent years, high-utility itemset mining has emerged as an important data mining task.
However, it remains computationally expensive both in terms of runtime and memory …

High utility itemset mining problem involves the use of internal and external utilities of items
(such as profits, margins) to discover interesting patterns from a given transactional …

Mining useful patterns from varied types of databases is an important research topic, which
has many reallife applications. Most studies have considered the frequency as sole …

Mining patterns with high utilization (or called high-utility itemset mining, HUIM) is
considered a major issue in recent decades especially in the market (eg, supermarket) …

High-utility itemset mining (HUIM) is an important data mining task with wide applications. In
this paper, we propose a novel algorithm named EFIM (EFficient high-utility Itemset Mining) …

High-utility itemset mining is a popular data mining problem that considers utility factors,
such as quantity and unit profit of items besides frequency measure from the transactional …