k -PFPMiner: Top-k Periodic Frequent Patterns in Big Temporal Databases
Palla Likhitha, Penugonda Ravikumar, Deepika Saxena, Rage Uday Kiran, Yutaka Watanobe · IEEE Access · 2023
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_supcontrols the least number of transactions in which a pattern must appear in a database.Max_percontrols 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 appropriatemin_supandmax_pervalues for any given database. This paper addresses this open problem by proposing a solution to discover top-kperiodic-frequent patterns in a temporal database. Top-kperiodic-frequent patterns representkperiodic-frequent patterns with the lowestperiodicityvalue in a database. An efficient depth-first search algorithm, Top-kPeriodic-Frequent Pattern Miner (k-PFPMiner), which takes onlykthreshold 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 memory and runtime-efficient and also highly scalable.