Language-Model Based Informed Partition of Databases to Speed Up Pattern Mining

Summary: Proposes language-model/word-embedding–driven horizontal partitioning for frequent itemset mining: treat transactions as sentences, items as words, then cluster to form informed partitions. Goal is not just parallelism, but shrinking per-partition vocabulary/entropy to make mining scalable on large, sparse databases (e.g., graph propositionalizations). (summarized by gpt-5.4-mini on May 24 2026)

Paper ID: 7009
Venue: SIGMOD
Year: 2024
Pagerank: 5.093636e-05
Overall Rank: 11,188 | 23.25%
DOI: 10.1145/3654987

Incoming Non-self Citations Over Time

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Authors

1. Carlos Bobed (Aragon Institute of Engineering Research; University of Zaragoza)
2. Jorge Bernad (Aragon Institute of Engineering Research; University of Zaragoza)
3. Pierre Maillot (CNRS; I3S; University Cote d’Azur Inria)

BibTeX Citation

@inproceedings{bobed_sigmod24,
        title = {{Language-Model Based Informed Partition of Databases to Speed Up Pattern Mining}},
        author = {Bobed, Carlos and Bernad, Jorge and Maillot, Pierre},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3654987},
        url = {https://dl.acm.org/doi/10.1145/3654987},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
27	Fast Algorithms for Mining Association Rules	1994	VLDB	0.00052255472

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