False Positive or False Negative: Mining Frequent Itemsets from High Speed Transactional Data Streams

Summary: Mining frequent itemsets from high-speed transactional streams under strict memory bounds; exponential itemset space (2^I-1) makes tracking hard. False-negative oriented algorithms with Chernoff-bound guarantees trade misses for bounded memory, achieving controllable recall and outperforming prior false-positive methods in accuracy, memory, and CPU time. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 9216
Venue: VLDB
Year: 2004
Pagerank: 6.1780147e-05
Overall Rank: 4,449 | 69.06%
DOI: -

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,584	Augmented Sketch: Faster and More Accurate Stream Processing	2016	SIGMOD	0.00011255801
4,089	On Dense Pattern Mining in Graph Streams [Extended Abstract]	2010	VLDB	6.4587806e-05
12,562	Using Association Rules for Fraud Detection in Web Advertising Networks	2005	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 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
166	Approximate Frequency Counts over Data Streams	2002	VLDB	0.00039361552
781	Spectral Bloom Filters	2003	SIGMOD	0.00016741046
865	What’s Hot and What’s Not: Tracking Most Frequent Items Dynamically	2003	PODS	0.00015808172

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