Default user detection plays one of the backbones in credit risk forecasting and
management. It aims at, given a set of corresponding features, eg, patterns extracted from …

Association rules are commonly used to provide decision‐makers with knowledge that helps
them to make good decisions. Most of the published proposals mine association rules …

Episode mining is a research area in data mining, where the aim is to discover interesting
episodes, that is, subsequences of events, in an event sequence. The most popular episode …

Recently, order-preserving pattern (OPP) mining, a new sequential pattern mining method,
has been proposed to mine frequent relative orders in a time series. Although frequent …

The aim of sequential pattern mining (SPM) is to discover potentially useful information from
a given sequence. Although various SPM methods have been investigated, most of these …

Discovering frequent trends in time series is a critical task in data mining. Recently, order-
preserving matching was proposed to find all occurrences of a pattern in a time series …

High average utility pattern mining is the concept proposed to complement drawbacks of
high utility pattern mining by considering lengths of patterns along with the utilities of the …

Sequential pattern mining (SPM) is an important branch of knowledge discovery that aims to
mine frequent sub-sequences (patterns) in a sequential database. Various SPM methods …

Fraud transactions obtain the rights and interests of e-commerce platforms by illegal ways,
and have been the emerging threats to the healthy development of these platforms …

Frequent Episode Mining (FEM), which aims at mining frequent sub-sequences from a
single long event sequence, is one of the essential building blocks for the sequence mining …