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An Effective Hash-Based Algorithm for Mining Association Rules

Summary: Hash-based candidate generation drastically reduces 2-itemset counts, addressing the primary bottleneck in large-itemset mining. Early trimming of the transaction DB shrinks work for later iterations, with simulations showing significant performance gains. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
2860
Venue
SIGMOD
Year
1995
Pagerank
0.0002366381
Overall Rank
240 | 98.36%
DOI
10.1145/223784.223813

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{park_sigmod95,
        title = {{An Effective Hash-Based Algorithm for Mining Association Rules}},
        author = {Park, Jong Soo and Chen, Ming-Syan and Yu, Philip S.},
        series = {{SIGMOD} '95},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/223784.223813},
        url = {https://dl.acm.org/doi/10.1145/223784.223813},
        year = {1995}
}

Incoming Citations (Sorted by Pagerank)

Showing 26 of 26 citing papers.

Rank Citing Paper Year Venue Pagerank
122 Approximate Frequency Counts over Data Streams 2002 VLDB 0.00031260115
161 Mining Frequent Patterns without Candidate Generation 2000 SIGMOD 0.00027981772
456 Discovery of Multiple-Level Association Rules from Large Databases 1995 VLDB 0.00018124452
460 Mining Generalized Association Rules 1995 VLDB 0.00018071773
462 Sampling Large Databases for Association Rules 1996 VLDB 0.00018065337
577 Computing Iceberg Queries Efficiently 1998 VLDB 0.00016235949
606 Mining Quantitative Association Rules in Large Relational Tables 1996 SIGMOD 0.00015804851
711 Beyond Market Baskets: Generalizing Association Rules to Correlations 1997 SIGMOD 0.00014713746
886 Efficiently Mining Long Patterns from Databases 1998 SIGMOD 0.0001340645
1,535 Exploratory Mining and Pruning Optimizations of Constrained Association Rules 1998 SIGMOD 0.00010463058
3,497 Interactive Analysis of Web-Scale Data 2009 CIDR 7.3634378e-05
3,969 Cache-conscious Frequent Pattern Mining on a Modern Processor 2005 VLDB 6.9837297e-05
4,092 Traversing Itemset Lattices with Statistical Metric Pruning 2000 PODS 6.907339e-05
4,154 A New SQL-like Operator for Mining Association Rules 1996 VLDB 6.8670958e-05
4,545 Association Rules over Interval Data 1997 SIGMOD 6.6382392e-05
4,565 Data Mining Using Two-Dimensional Optimized Association Rules: Scheme, Algorithms, and Visualization 1996 SIGMOD 6.6278063e-05
4,766 Mining Optimized Association Rules for Numeric Attributes 1996 PODS 6.5181854e-05
4,969 Optimization of Constrained Frequent Set Queries with 2-variable Constraints 1999 SIGMOD 6.4201643e-05
5,034 Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases 1998 VLDB 6.3930337e-05
6,280 Efficient Construction of Regression Trees with Range and Region Splitting 1997 VLDB 5.928365e-05
6,689 Ratio Rules: A New Paradigm for Fast, Quantifiable Data Mining 1998 VLDB 5.8014872e-05
7,044 On the Discovery of Interesting Patterns in Association Rules 1998 VLDB 5.7187358e-05
7,210 A Framework for Measuring Changes in Data Characteristics 1999 PODS 5.6720634e-05
9,124 Ontological Pathfinding: Mining First-Order Knowledge from Large Knowledge Bases 2016 SIGMOD 5.319248e-05
12,656 Optimization of Frequent Itemset Mining on Multiple-Core Processor 2007 VLDB 5.093636e-05
12,882 Mining Frequent Itemsets Using Support Constraints 2000 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank Cited Paper Year Venue Pagerank
13 Mining Association Rules between Sets of Items in Large Databases 1993 SIGMOD 0.0006567919
88 Efficient and Effective Clustering Methods for Spatial Data Mining 1994 VLDB 0.00035240327
349 Knowledge Discovery in Databases: An Attribute-Oriented Approach 1992 VLDB 0.00020432737
440 An Interval Classifier for Database Mining Applications 1992 VLDB 0.00018409145
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