Frequent pattern mining (FM) has a wide range of applications in the real world. But FM
sometimes discovers many uninteresting patterns at the same time. Constraint-based FM …

Periodic frequent pattern discovery is a non-trivial task to discover frequent patterns based
on user interests using a periodicity measure. Although conventional algorithms for periodic …

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 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 …

A geo-referenced time series database represents the data generated by a set of fixed
locations (or items) observing a particular phenomenon over time. Useful information that …

In the era of big data, dynamic data have become more popular than static data because
high volumes of data can be generated and collected at a rapid rate. Although rough set …

Extracting stable periodic-frequent patterns in very large temporal databases is a key task in
big data analytics. Existing studies have mainly concentrated on discovering these patterns …

Partial periodic pattern (3P) mining is a vital data mining technique that aims to discover all
interesting patterns that have exhibited partial periodic behavior in temporal databases …

Discovering periodic-frequent patterns in temporal databases is a challenging data mining
problem with abundant applications. It involves discovering all patterns in a database that …

Periodic frequent-pattern mining (PFPM) is a vital knowledge discovery technique that
identifies periodically occurring patterns in a temporal database. Although traditional PFPM …