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 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 …

High-utility itemset mining (HUIM) is an emerging data science approach that comprises
discovered itemsets having huge significance in the database. HUIM Please check and …

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 …

Abstract A High Utility Itemset (HUI) mining is an important problem in the data mining
literature that considers utilities of items (such as profits and margins) to discover interesting …

In recent years, high utility itemsets (HUIs) mining from the transactional databases becomes
one of the most emerging research topic in the field of data mining due to its wide range of …

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 …

Data mining techniques discover the potentially helpful pattern concealed in a vast database
for the decision support system in various real-life applications. One such technique is …

HUIM (High utility itemsets mining) is a sub-division of data mining dealing with the task to
obtain promising patterns in the quantitative datasets. A variant of HUIM is to discover the …

Abstract High Utility Itemset Mining (HUIM) is the task of analyzing customer transactions to
find the sets of items that yield a high utility (eg profit). A major limitation of traditional HUIM …