k-PFPMiner: Top-k Periodic Frequent Patterns in Big Temporal Databases

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 satisfy the user-specified minimum support (
$min{\_{}}sup$
) and maximum periodicity (
$max$
_
$per$
) constraints.
$Min\_{}sup$
controls the least number of transactions in which a pattern must appear in a database.
$Max\_{}per$
controls the maximum time interval within which a pattern must reappear in the database. The popular adoption of this task has been hindered by an open problem, which involves setting appropriate
$min\_{}sup$
and
$max\_{}per$
values for any given database. This paper addresses this open problem by proposing a solution to discover top-
$k$
periodic-frequent patterns in a temporal database. Top-
$k$
periodic-frequent patterns represent the
$k$
number of periodic-frequent patterns having the lowest
$periodicity$
value in a database. An efficient depth-first search algorithm, Top-
$k$
Periodic-Frequent Pattern Miner (
$k$
-PFPMiner), which takes only
$k$
threshold as an input, was presented to find all desired patterns in a database. Experimental results on synthetic and real-world databases demonstrate that our algorithm is efficient and scalable.