<italic>k</italic>-PFPMiner: Top-k Periodic Frequent Patterns in Big Temporal Databases

Abstract

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.

Keywords

• Data mining
• pattern mining
• temporal databases
• top-k
• periodic-frequent patterns