Nonoverlapping sequential pattern mining is an important type of sequential pattern mining
(SPM) with gap constraints, which not only can reveal interesting patterns to users but also …

For sequence classification, an important issue is to find discriminative features, where
sequential pattern mining (SPM) is often used to find frequent patterns from sequences as …

The information technology revolution, especially with the adoption of the Internet of Things,
longitudinal data in many domains become more available and accessible for secondary …

Sequential pattern mining (SPM) has been applied in many fields. However, traditional SPM
neglects the pattern repetition in sequence. To solve this problem, gap constraint SPM was …

Nonoverlapping 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 …

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 …

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 …

Gap constraint sequential pattern mining (SPM), as a kind of repetitive SPM, can avoid
mining too many useless patterns. However, this method is difficult for users to set a suitable …

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

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 …