Sequential Pattern Mining (SPM) is a branch of data mining that deals with finding
statistically relevant regularities of patterns in sequentially ordered data. It has been an …

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 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 …

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

Most of the periodic pattern mining algorithms extract fully periodic patterns by strictly
monitoring the cyclic behaviour of patterns in transactional as well as temporal databases …

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 …

Finding periodic-frequent patterns in temporal databases is a prominent data mining
problem with bountiful applications. It involves discovering all patterns in a database that …

Periodic-frequent patterns are a vital class of regularities in a temporal database. Most
previous studies followed the approach of finding these patterns by storing the temporal …