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 …

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 …

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 …

Existing API mining algorithms can be difficult to use as they require expensive parameter
tuning and the returned set of API calls can be large, highly redundant and difficult to …

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 …

A growing number of universities worldwide use various forms of online and blended
learning as part of their academic curricula. Furthermore, the recent changes caused by the …

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 …

Sequence pattern mining aims to discover frequent subsequences as patterns in a single
sequence or a sequence database. By combining gap constraints (or flexible wildcards) …

An ideal outcome of pattern mining is a small set of informative patterns, containing no
redundancy or noise, that identifies the key structure of the data at hand. Standard frequent …

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