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 …

Itemset mining is an important subfield of data mining, which consists of discovering
interesting and useful patterns in transaction databases. The traditional task of frequent …

Frequent pattern mining has become very useful and interesting to researchers due to its
high applicability. Different real-life databases (eg, sensor network, medical diagnosis data) …

Testing deep learning (DL) software is crucial and challenging. Recent approaches use
differential testing to cross-check pairs of implementations of the same functionality across …

Periodic pattern mining is a popular data mining task, which consists of identifying patterns
that periodically appear in data. Traditional periodic pattern mining algorithms are designed …

Frequent itemset mining is a fundamental element with respect to many data mining
problems directed at finding interesting patterns in data. Recently the PrePost algorithm, a …

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 …

In the Internet age, analyzing the behavior of online users can help webstore owners
understand customers' interests. Insights from such analysis can be used to improve both …

Mining sequential patterns is used to discover all the frequent sequences in a sequence
database. However, the mining may return a huge number of patterns, while the users are …

Nowadays, raw data is rarely used directly. In real world applications, data is often
processed, and the necessary knowledge extracted, depending on the purpose of the user …