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: 9659
Venue: VLDB
Year: 2008
Pagerank: 4.790704e-05
Overall Rank: 7,246 | 49.60%
DOI: -

Incoming Non-self Citations Over Time

Authors

1. Alexander Lachmann
2. Mirek Riedewald

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,716	Mining Graph Patterns Efficiently via Randomized Summaries	2009	VLDB	5.9755569e-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
36	Fast Algorithms for Mining Association Rules	1994	VLDB	0.00076161096

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