Discovering unexpected and useful patterns in databases is a fundamental data mining task.
In recent years, a trend in data mining has been to design algorithms for discovering …

Sequential pattern mining algorithms using a vertical representation are the most efficient for
mining sequential patterns in dense or long sequences, and have excellent overall …

Data Mining in general is the act of extracting interesting patterns and discovering non-trivial
knowledge from a large amount of data. Medical data mining can be used to understand the …

Negative sequential patterns (NSPs) produced by negative sequence analysis (NSA)
capture more informative and actionable knowledge than classic positive sequential …

In recent years, sequential pattern mining (SPM) has been applied in many domains,
including recommender systems, fraud detection, and other related industries. Target …

Pattern mining has been an attractive topic for many researchers since its first introduction.
Clickstream mining, a specific version of sequential pattern mining, has been shown to be …

We present a unifying view of temporal concepts and data models in order to categorize
existing approaches for unsupervised pattern mining from symbolic temporal data. In …

Although there have been many recent studies on the mining of sequential patterns in a
static database and in a database with increasing data, these works, in general, do not fully …

Negative sequential patterns (NSPs) capture more informative and actionable knowledge
than classic positive sequential patterns (PSPs) due to the involvement of both occurring …

Sequential data are generated in many domains of science and technology. Although many
studies have been carried out for sequence classification in the past decade, the problem is …