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 …

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 …

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 …

Sequential Pattern Mining (SPM) is a branch of data mining that deals with finding
statistically relevant regularities of patterns in sequentially ordered data. It has been an …

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 …

Nonoverlapping sequential pattern mining (SPM) is a type of SPM with gap constraints that
can mine valuable information in sequences. One of the disadvantages of nonoverlapping …

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 …

Due to the rapid development of science and technology, the importance of imprecise, noisy,
and uncertain data is increasing at an exponential rate. Thus, mining patterns in uncertain …