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 …

Processing changeable data streams in real time is one of the most important issues in the
data mining field due to its broad applications such as retail market analysis, wireless sensor …

The mining frequent itemsets plays an important role in the mining of association rules.
Frequent itemsets are typically mined from binary databases where each item in a …

Periodic frequent patterns are sets of events or items that periodically appear in a sequence
of events or transactions. Many algorithms have been designed to identify periodic frequent …

Large graphs are often used to simulate and model complex systems in various research
and application fields. Because of its importance, frequent subgraph mining (FSM) in single …

In some situations, finding the rare association rule is of higher importance than the frequent
itemset. Unique rules represent rare cases, activities, or events in real-world applications. It …

Formal concept analysis (FCA) visualizes formal concepts in terms of a concept lattice.
Usually, it is an NP‐problem and consumes plenty of time and storage space to update the …

Frequent itemsets mining is an active research problem in the domain of data mining and
knowledge discovery. With the advances in database technology and an exponential …

Frequent closed patterns (FCPs), a condensed representation of frequent patterns, have
been proposed for the mining of (minimal) non-redundant association rules to improve …