A Condensed Representation to Find Frequent Patterns

Summary: Introduce disjunction-free sets as a condensed representation of frequent patterns that enables exact regeneration of all frequent itemsets and their frequencies without accessing the original data. Provide efficient extraction algorithms that empirically beat frequent closed sets, often with much lower cost even in hard cases. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: h6ff8ce8bb319c288
Venue: PODS
Year: 2001
Pagerank: 5.2905577e-05
Overall Rank: 8,724 | 41.35%
DOI: 10.1145/375551.375604

Incoming Non-self Citations Over Time

Authors

1. Artur Bykowski (Institut National des Sciences Appliquées de Lyon)
2. Christophe Rigotti (Institut National des Sciences Appliquées de Lyon)

BibTeX Citation

@inproceedings{bykowski_pods01,
        address = {New York, NY, USA},
        series = {{PODS} '01},
        title = {{A Condensed Representation to Find Frequent Patterns}},
        url = {https://dl.acm.org/doi/10.1145/375551.375604},
        doi = {10.1145/375551.375604},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Bykowski, Artur and Rigotti, Christophe},
        year = {2001}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
13,016	Differential Constraints	2005	PODS	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
164	Mining Frequent Patterns without Candidate Generation	2000	SIGMOD	0.00027412227
473	Sampling Large Databases for Association Rules	1996	VLDB	0.00017673931
912	Efficiently Mining Long Patterns from Databases	1998	SIGMOD	0.00013115246

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