Abstract The High Average Utility Itemset Mining (HAUIM) technique, a variation of High
Utility Itemset Mining (HUIM), uses the average utility of the itemsets. Historically, most …

One-off sequential pattern mining (SPM)(or SPM under the one-off condition) is a kind of
repetitive SPM with gap constraints, and has been widely applied in many fields. However …

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 …

Discovering high average utility itemsets (HAUIs) in a quantitative database is a popular
data mining task, which aims at identifying sets of products (items) purchased together that …

Traditional partial periodic pattern mining algorithms tend to work on a single time series or
database. However, time series databases usually consist of interrelated multivariate time …

Abstract High Utility Pattern Mining is a popular task for analyzing data. It consists of
discovering patterns having a high importance in databases. A popular application of high …

Frequent pattern mining (FM) has a wide range of applications in the real world. But FM
sometimes discovers many uninteresting patterns at the same time. Constraint-based FM …

Repetitive sequential pattern mining (SPM) with gap constraints is a data analysis task that
consists of identifying patterns (subsequences) appearing many times in a discrete …

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 …

High average utility pattern mining has been proposed to overcome the demerits of high
utility pattern mining. Since high average utility pattern mining can extract more valuable …