Nonoverlap** sequential pattern mining (SPM) is a data analysis task, which aims at
identifying repetitive sequential patterns with gap constraint in a set of discrete sequences …

Traditional sequential pattern mining methods were designed for symbolic sequence. As a
collection of measurements in chronological order, a time series needs to be discretized into …

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 …

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 …

Nonoverlap** sequential pattern mining, as a kind of repetitive sequential pattern mining
with gap constraints, can find more valuable patterns. Traditional algorithms focused on …

The three-way decisions strategy has been employed to construct network topology in a
single hidden layer feedforward neural network (SFNN). However, this model has a general …

Generic sequential pattern mining problem aims to mine the set of sequential patterns from a
sequential database that satisfies a minimum support or occurrence threshold constraint …

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 …

Sequential pattern mining (SPM) is a useful tool for extracting implicit and meaningful rules
from sequence datasets that can aid the decision-making process. These rules are ordered …