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 itemsets (HUIs) mining is an emerging topic in data mining, which refers to
discovering all itemsets having a utility meeting a user-specified minimum utility threshold …

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 …

Data mining methods have been required in both commercial and non-commercial areas. In
such circumstances, pattern mining techniques can be used to find meaningful pattern …

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 …

Processing changeable data streams in real time is one of the most important issues in the
data mining field due to its broad applications such as retail market analysis, wireless sensor …

High utility pattern mining has been actively researched as one of the significant topics in the
data mining field since this approach can solve the limitation of traditional pattern mining that …

The traditional methods for mining association rules (ARs) include two phrases: mining
frequent itemsets (FIs)/frequent closed itemsets (FCIs)/frequent maximal itemsets (FMIs) and …

Since traditional frequent pattern mining approaches assume that all the items in binary
databases have the same importance regardless of their own features, they have difficulty in …

Databases that deal with the real world have various characteristics. New data is
continuously inserted over time without limiting the length of the database, and a variety of …