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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
6947
Venue
SIGMOD
Year
2024
Pagerank
4.1945683e-05
Overall Rank
10,975 | 23.65%
DOI
10.1145/3654987

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Rank Cited Paper Year Venue Pagerank
36 Fast Algorithms for Mining Association Rules 1994 VLDB 0.00076161096
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