Beyond Itemsets: Mining Frequent Featuresets over Structured Items
Summary: Extends frequent itemset mining to structured items by using per-item feature sets and mining frequent featuresets from user sessions. Proposes a featureset-uncertainty model with probabilistic featuresets learned by constrained least squares, plus diverse mining algorithms and experimental evaluation. (summarized by gpt-5-nano on Feb 09 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 13 | Mining Association Rules between Sets of Items in Large Databases | 1993 | SIGMOD | 0.0010864752 |
| 36 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00076161096 |
| 101 | ULDBs: Databases with Uncertainty and Lineage | 2006 | VLDB | 0.0004955674 |
| 473 | Sampling Large Databases for Association Rules | 1996 | VLDB | 0.0002233798 |
| 5,796 | Finding Frequent Items in Probabilistic Data | 2008 | SIGMOD | 5.3240234e-05 |
| 7,633 | Mining Frequent Itemsets over Uncertain Databases | 2012 | VLDB | 4.6914549e-05 |
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