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 …

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 …

Closed high utility itemsets (CHUIs) and maximal high utility itemsets (MaxHUIs) are two
important concise representations of HUIs. Discovering these itemsets is important because …

Negative sequential pattern mining (SPM) is an important SPM research topic. Unlike
positive SPM, negative SPM can discover events that should have occurred but have not …

Detecting sets of relevant patterns from a given dataset is an important challenge in data
mining. The relevance of a pattern, also called utility in the literature, is a subjective measure …

Traditional high-utility itemset mining (HUIM) aims to determine all high-utility itemsets
(HUIs) that satisfy the minimum utility threshold in transaction databases. However, in most …

The discovery of frequent closed high utility itemsets (FCHUIs) and frequent generators of
high utility itemsets (FGHUIs) is significant because they serve as important and concise …

As a method of extracting important information in the real world, the field of pattern mining
has been actively studied. In particular, in industrial fields such as factories, it is also …

Mining high-utility patterns in databases containing items with both positive and negative
profits is useful in market basket databases, since negative profits are common in the real …

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 …