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 an important task in data mining that can retrieve more
meaningful and useful patterns for decision-making. One-phase HUIM algorithms based on …

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 (HUIM) is a popular data mining task with applications in
numerous domains. However, traditional HUIM algorithms often produce a very large set of …

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 …

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 …

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 has emerged to be an important research issue in data mining
since it has a wide range of real life applications. Although a number of algorithms have …

High utility itemset mining (HUIM) is a popular and important mining task in recent years.
The problem is considered computational expensive in terms of execution time and memory …

High utility itemset mining is an emerging data mining task, which consists of discovering
highly profitable itemsets (called high utility itemsets) in very large transactional databases …