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 …

High-utility pattern mining is an important sub-literature in the data mining literature. This
literature discusses the discovery of useful pattern information from large databases by …

The problem of mining high utility sequences aims at discovering subsequences having a
high utility (importance) in a quantitative sequential database. This problem is a natural …

Negative sequential patterns (NSPs) produced by negative sequence analysis (NSA)
capture more informative and actionable knowledge than classic positive sequential …

Sequence pattern discovery is a fundamental topic in the domain of data mining. It has been
widely used to solve various problems (eg, behavior pattern discovery, gene pattern …

Mining all frequent high utility sequences (FHUS) in quantitative sequential databases
(QSDBs) is a generalization of the problem of mining all frequent sequences in non …

High utility itemset mining (HUIM) with negative utility is an emerging data mining task.
However, the setting of the minimum utility threshold is always a challenge when mining …

High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its
wide application and considerable popularity. However, due to the combinatorial explosion …

The goal of high-utility sequential pattern mining (HUSPM) is to efficiently discover profitable
or useful sequential patterns in a large number of sequences. However, simply being aware …

Sequential pattern mining is concerned with the extraction of frequent or recurrent
behaviors, modeled as subsequences, from a sequence dataset. Such patterns inform about …