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

被引：0
作者：
Likhitha, Palla [1 ]
Ravikumar, Penugonda [1 ]
Saxena, Deepika [1 ]
Kiran, Rage Uday [1 ]
Watanobe, Yutaka [1 ]
机构：
[1] Univ Aizu, Div Informat Syst, Aizu Wakamatsu, Fukushima 9650006, Japan
来源：
IEEE ACCESS | 2023年 / 11卷
基金：
日本学术振兴会;
关键词：
Data mining; pattern mining; temporal databases; top-k; periodic-frequent patterns; ALGORITHMS;
D O I：
10.1109/ACCESS.2023.3325839
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Finding periodic-frequent patterns in temporal databases is a prominent data mining problemwith bountiful applications. It involves discovering all patterns in a database that satisfy the user-specifiedminimum support(min_sup) and maximum periodicity(max_per) constraints.Min_supcontrols the leastnumber of transactions in which a pattern must appear in a database.Max_percontrols the maximumtime interval within which a pattern must reappear in the database. The popular adoption of this task hasbeen hindered by an open problem, which involves setting appropriatemin_supandmax_pervalues forany 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 represent theknumberof periodic-frequent patterns having the lowestperiodicityvalue in a database. An efficient depth-first searchalgorithm, 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-worlddatabases demonstrate that our algorithm is efficient and scalable.
引用
收藏
页码：119033 / 119044
页数：12
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