High utility itemset mining (HUIM) has emerged as an important research topic in data
mining, with applications to retail-market data analysis, stock market prediction, 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 …

HUIM (High utility itemset mining) is a key problem in data mining. The goal is to find
itemsets having a high importance or profit in a database, to identify useful knowledge that …

High-utility itemset mining (HUIM) is a useful tool for analyzing customer behavior in the field
of data mining. HUIM algorithms can discover the most beneficial itemsets in transaction …

Mining high utility itemsets are the basic task in the area of frequent itemset mining (FIM) that
has various applications in diverse domains, including market basket analysis, web mining …

High utility itemsets are sets of items having a high utility or profit in a database. Efficiently
discovering high utility itemsets plays a crucial role in real-life applications such as market …

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) …

Mining high utility itemsets is considered to be one of the important and challenging
problems in the data mining literature. The problem offers greater flexibility to a decision …

High-utility itemset mining (HUIM) in transaction databases is an important data mining task
with wide applications. However, most HUIM algorithms assume the unrealistic assumption …

High-utility itemset (HUI) mining is a popular data mining task, consisting of enumerating all
groups of items that yield a high profit in a customer transaction database. However, an …