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 …

Frequent patterns are an important class of regularities that exist in a transaction database.
Certain frequent patterns with low minimum support (minsup) value can provide useful …

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 …

Recently, temporal occurrences of the frequent patterns in a transactional database has
been exploited as an interestingness criterion to discover a class of user-interest-based …

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 …

Periodic-frequent patterns are an important class of regularities that exist in a transactional
database. Informally, a frequent pattern is said to be periodic-frequent if it appears at a …

Healthcare applications in Internet of Things (IoT) systems have been increasingly
researched because they facilitate remote monitoring of patients. Though IoT may create …