A periodic high-utility sequential pattern (PHUSP) is a pattern that not only yields a high-
utility (eg high profit) but also appears regularly in a sequence database. Finding PHUSPs is …

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 …

Mining association patterns from a time-stamped temporal database distributed over finite
time slots is implicitly associated with task of scanning the input database. Finding supports …

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 …

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 …

The understanding of various health-oriented vital sign data generated from body sensor
networks (BSNs) and discovery of the associations between the generated parameters is an …

Deriving insight from data is a challenging task for researchers and practitioners, especially
when working on spatio-temporal domains. If pattern searching is involved, the …

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 …