PSMSP: A Parallelized Sampling-Based Approach for Mining Top-k Sequential Patterns in Database Graphs
| Autor: | Yang Jincui, Binxing Fang, Xi Zhang, Mingtao Lei |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Vertex (graph theory)
050101 languages & linguistics Database Computer science 05 social sciences Database graph 02 engineering and technology computer.software_genre 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Sequential Pattern Mining Database transaction computer |
| Zdroj: | Database Systems for Advanced Applications ISBN: 9783030185893 DASFAA (3) |
| DOI: | 10.1007/978-3-030-18590-9_23 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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