DBScholar

Back to papers

Mining Frequent Itemsets Using Support Constraints

Summary: Introduces constrained frequent-itemset mining where minimum support varies by itemset, avoiding uniform-support Apriori’s missed low-support patterns and excessive generation. Pushes support constraints into candidate generation, selecting each itemset’s best runtime threshold while retaining Apriori pruning. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
8824
Venue
VLDB
Year
2000
Pagerank
5.093636e-05
Overall Rank
12,882 | 11.62%
DOI
-

Incoming Non-self Citations Over Time

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

Authors

BibTeX Citation

@article{wang_vldb00,
        title = {{Mining Frequent Itemsets Using Support Constraints}},
        author = {Wang, Ke and He, Yu and Han, Jiawei},
        journal = {PVLDB},
        series = {{VLDB} '00},
        pages = {43--52},
        year = {2000}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank Citing Paper Year Venue Pagerank
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

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

Previous Page 1 / 1 Next

Semantically Similar Papers