Mining Frequent Itemsets Using Support Constraints

Summary: Framework for mining frequent itemsets with support constraints, enabling nonuniform minimum support across itemsets. By pushing constraints into Apriori generation, it uses per-itemset run-time minimums to prune search and preserve Apriori efficiency. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 8634
Venue: VLDB
Year: 2000
Pagerank: 4.1905499e-05
Overall Rank: 12,698 | 11.75%
DOI: -

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Rank	Citing Paper	Year	Venue	Pagerank

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Showing 11 of 11 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
13	Mining Association Rules between Sets of Items in Large Databases	1993	SIGMOD	0.0010863639
36	Fast Algorithms for Mining Association Rules	1994	VLDB	0.00076114894
117	An Effective Hash-Based Algorithm for Mining Association Rules	1995	SIGMOD	0.00045914132
182	Mining Frequent Patterns without Candidate Generation	2000	SIGMOD	0.00036955562
229	Discovery of Multiple-Level Association Rules from Large Databases	1995	VLDB	0.0003226623
404	Mining Generalized Association Rules	1995	VLDB	0.00024159651
546	An Efficient Algorithm for Mining Association Rules in Large Databases	1995	VLDB	0.00020410312
651	Dynamic Itemset Counting and Implication Rules for Market Basket Data	1997	SIGMOD	0.00018649942
735	Beyond Market Baskets: Generalizing Association Rules to Correlations	1997	SIGMOD	0.00017417893
2,330	Building Hierarchical Classifiers Using Class Proximity	1999	VLDB	9.0229333e-05
5,426	A New Framework For Itemset Generation	1998	PODS	5.5131484e-05

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11,960	Beyond Itemsets: Mining Frequent Featuresets over Structured Items	2015	VLDB	4.1905499e-05