Finding Relevant Patterns in Bursty Sequences

Summary: Novel transformation of bursty sequences reduces irrelevant repetitive patterns and mining cost. Transformed data remain compatible with existing mining algorithms; at fixed support, results are faster, smaller, and faithful to the original patterns. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 9850
Venue: VLDB
Year: 2008
Pagerank: 5.5664827e-05
Overall Rank: 7,691 | 47.24%
DOI: 10.14778/1453856.1453870

Incoming Non-self Citations Over Time

Authors

1. Alexander Lachmann (RWTH Aachen University)
2. Mirek Riedewald (Cornell University)

BibTeX Citation

@article{lachmann_vldb08,
        title = {{Finding Relevant Patterns in Bursty Sequences}},
        author = {Lachmann, Alexander and Riedewald, Mirek},
        journal = {PVLDB},
        series = {{VLDB} '08},
        volume = {1},
        number = {1},
        pages = {78--89},
        doi = {10.14778/1453856.1453870},
        url = {https://doi.org/10.14778/1453856.1453870},
        year = {2008}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,581	Mining Graph Patterns Efficiently via Randomized Summaries	2009	VLDB	6.6198548e-05

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
27	Fast Algorithms for Mining Association Rules	1994	VLDB	0.00052255472

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