DP-gSpan: A Pattern Growth-based Differentially Private Frequent Subgraph Mining Algorithm

Xing, Jiangna; Ma, Xuebin

doi:10.1109/trustcom53373.2021.00067

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Periodic sequential pattern mining [20,21] is used to fnd patterns that occur frequently and periodically in long sequences. Subgraph mining [22,23] is another feld of sequential pattern mining, which aims to discover all frequent subgraphs in graph databases, the corresponding algorithms based on diferent data structures (such as liststructure [24], pattern-tree [25], and optimization algorithm [19]) are proposed to solve the related pattern mining problem from sequences database, and all these pattern mining approaches are based on the sequential database and the constraints threshold (selected by the user). Recently, Wang gives Miner-K [25] algorithm to mine the patterns with length constraints, and Nader proposes NEclat-Closed [24] to mine the closed pattern based on a vertical structure.…”

Section: Related Workmentioning

confidence: 99%

Discovering Significant Sequential Patterns in Data Stream by an Efficient Two-Phase Procedure

Tang

Wang

Liu

et al. 2022

Mathematical Problems in Engineering

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One essential topic of mining sequential patterns in the data stream is to optimize the time-space computations. However, more importantly, it should pay more attention to the significance of mining results as a large portion of them just response to the user-defined constraints purely by accident and they may have no statistical significance. In this paper, we propose FSSPDS, an efficient two-phase algorithm to discover the significant sequential patterns (SSPs) in the data stream with typical sliding windows, which has never been considered in existing problems. First, for generating SSPs candidates with high-quality, FSSPDS takes testable support and pattern length constraints into account and insignificant patterns were removed timely by a pattern-growth method. In the second phase, appropriate permutation testing is used to test the significance of the SSPs candidates. Exact permutation p values are obtained in a novel combination way based on unconditional Barnard’s test statistic which better reflects the process of data generations and collections. Experimental evaluations show that FSSPDS allows the discovery of SSPs in the data stream and rivals the state-of-the-art approaches efficiently under the control of family-wise error rate (FWER), especially for time efficiency, which was approximately an order of magnitude higher.

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