Feasible Itemset Distributions in Data Mining: Theory and Application

Summary: Characterizes feasible length distributions of frequent and maximal itemset collections and derives tight lower bounds on achievable distributions. Applies these bounds to generate realistic synthetic datasets for benchmarking, linking pattern distribution to mining resource costs. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 1300
Venue: PODS
Year: 2003
Pagerank: 4.4039656e-05
Overall Rank: 9,064 | 36.95%
DOI: -

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
12,354	An Audit Environment for Outsourcing of Frequent Itemset Mining	2009	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
181	Mining Frequent Patterns without Candidate Generation	2000	SIGMOD	0.00036992674
547	An Efficient Algorithm for Mining Association Rules in Large Databases	1995	VLDB	0.00020420717
657	Dynamic Itemset Counting and Implication Rules for Market Basket Data	1997	SIGMOD	0.00018553891
840	Efficiently Mining Long Patterns from Databases	1998	SIGMOD	0.00016058396
6,465	Data mining, Hypergraph Transversals, and Machine Learning	1997	PODS	5.0530117e-05

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