We study to improve the efficiency of finding top-k sequential patterns in database graphs, where each edge (or vertex) is associated with multiple transactions and a transaction consists of a set of items. This task is to discover the subsequences of transaction sequences that frequently appear in many paths. We propose PSMSP, a Parallelized Sampling-based Approach For Mining Top-k Sequential Patterns, which involves: (a) a parallelized unbiased sequence sampling approach, and (b) a novel PSP-Tree structure to efficiently mine the patterns based on the anti-monotonicity properties. We validate our approach via extensive experiments with real-world datasets.
CITATION STYLE
Lei, M., Zhang, X., Yang, J., & Fang, B. (2019). PSMSP: A Parallelized Sampling-Based Approach for Mining Top-k Sequential Patterns in Database Graphs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11448 LNCS, pp. 254–258). Springer Verlag. https://doi.org/10.1007/978-3-030-18590-9_23
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