Periodic-frequent patterns (or itemsets) are an important class of regularities that exist in a
transactional database. Finding these patterns involves discovering all frequent patterns that …

Mining high-utility patterns in databases containing items with both positive and negative
profits is useful in market basket databases, since negative profits are common in the real …

Discovering periodic-frequent patterns in temporal databases is a challenging problem of
great importance in many real-world applications. Though several algorithms were …

Time and frequency are two important dimensions to determine the interestingness of a
pattern in a database. Periodic-frequent patterns are an important class of regularities that …

Partial periodic patterns are an important class of regularities that exist in a time series. A
key property of these patterns is that they can start, stop, and restart anywhere within a …

Stable periodic-frequent itemset mining is essential in big data analytics with many real-
world applications. It involves extracting all itemsets exhibiting stable periodic behaviors in a …

A temporal database is a collection of transactions, ordered by their timestamps. Discovering
partial periodic itemsets in temporal databases has numerous applications. However, to the …

Periodic pattern mining is an emerging technique for knowledge discovery. Most previous
approaches have aimed to find only those patterns that exhibit full (or perfect) periodic …

Frequent-regular pattern mining has attracted recently many works. Most of the approaches
focus on discovering a complete set of patterns under the user-given support and regularity …

The support and periodicity are two important dimensions to determine the interestingness
of a pattern in a dataset. Periodic-frequent patterns are an important class of regularities that …