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 pattern mining is an emerging data science task, which consists of discovering
patterns having a high importance in databases. The utility of a pattern can be measured in …

Discovering high utility itemsets in transaction databases is a key task for studying the
behavior of customers. It consists of finding groups of items bought together that yield a high …

Abstract High-Utility Itemset Mining (HUIM) is considered a major issue in recent decades
since it reveals profit strategies for use in industry for decision-making. Most existing works …

High-utility itemset mining (HUIM) is one of the heavily studied fields of data mining, which is
due to its high compatibility with real-world applications. HUIM is a process of extracting a …

Abstract The study of High-Utility Itemset Mining (HUIM) and Frequent Itemset Mining (FIM)
is crucial since it explains consumer behavior and offers actionable advice to improve …

HUIM has been an important issue in recent years, particularly in basket-market analysis,
since it identifies useful information or goods for decision-making. Numerous research …

The task of mining high-utility itemsets in a database given a minimum threshold is attracting
more and more interest due to its many applications. Existing algorithms such as the vertical …

The problem of discovering high-utility itemsets (HUIs) in transaction databases, which is an
extension of Frequent Itemset Mining, is a commonly encountered mining task. Researchers …

High utility itemset mining considers unit profits and quantities of items in a transaction
database to extract more applicable and more useful association rules. Downward closure …