Pattern mining is a key subfield of data mining that aims at developing algorithms to discover
interesting patterns in databases. The discovered patterns can be used to help …

Finding high-importance patterns in data is an emerging data mining task known as High-
utility itemset mining (HUIM). Given a minimum utility threshold, a HUIM algorithm extracts all …

In recent years, sequential pattern mining (SPM) has been applied in many domains,
including recommender systems, fraud detection, and other related industries. Target …

Sequential pattern mining (SPM) is an important technique in the field of pattern mining,
which has many applications in reality. Although many efficient SPM algorithms have been …

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 …

Significant efforts have been expended in the research and development of a database
management system (DBMS) that has a wide range of applications for managing an …

With the widespread application of efficient pattern mining algorithms, sequential patterns
that allow gap constraints have become a valuable tool to discover knowledge from …

This paper provides an overview of the current state-of-the-art on using constraints in
knowledge discovery and data mining. The use of constraints in a data mining task requires …

High utility sequential pattern mining (HUSPM) is a critical research topic in data analytics
(eg, smart-city technologies), which takes into consideration three pivotal factors of data …

In many real-world applications, sequential rule mining (SRM) can provide prediction and
recommendation functions for a variety of services. It is an important technique of pattern …