Mining Frequent Itemsets with Bit Strings and Trie

Summary: Bit-string encoding of transactions in a trie where each node stores an m-bit bitmap and a counter; initial scan finds frequent 1-items. Candidate itemsets are generated across multiple lattice levels in p passes (p < n), reducing I/O and memory via compact bitmaps and efficient trie traversal. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 8940
Venue: VLDB
Year: 2002
Pagerank: -
Overall Rank: 13,796 | 4.03%
DOI: -

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Nuansri Denwattana

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank

Semantically Similar Papers

Overall Rank	Paper	Year	Venue	Pagerank
2,685	On Differentially Private Frequent Itemset Mining	2013	VLDB	8.3070708e-05
12,095	A Performance Study of Three Disk-based Structures for Indexing and Querying Frequent Itemsets	2013	VLDB	4.1945683e-05
12,689	Mining Frequent Itemsets Using Support Constraints	2000	VLDB	4.1945683e-05
9,064	Feasible Itemset Distributions in Data Mining: Theory and Application	2003	PODS	4.4039656e-05
166	Approximate Frequency Counts over Data Streams	2002	VLDB	0.00039361552
117	An Effective Hash-Based Algorithm for Mining Association Rules	1995	SIGMOD	0.00045896865
11,978	Resource-oriented Approximation for Frequent Itemset Mining from Bursty Data Streams	2014	SIGMOD	4.1945683e-05
181	Mining Frequent Patterns without Candidate Generation	2000	SIGMOD	0.00036992674
835	Finding Frequent Items in Data Streams	2008	VLDB	0.00016109621
4,449	False Positive or False Negative: Mining Frequent Itemsets from High Speed Transactional Data Streams	2004	VLDB	6.1780147e-05