Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases

Summary: Mining association rules for near-optimal binary database segmentations under convex objectives, proving entropy, χ², and Gini criteria qualify. Computational-geometry algorithms extend beyond binary targets and scale to huge categorical databases. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 8695
Venue: VLDB
Year: 1998
Pagerank: 6.3930337e-05
Overall Rank: 5,034 | 65.47%
DOI: -

Incoming Non-self Citations Over Time

Authors

1. Yasuhiko Morimoto (International Business Machines Tokyo Research Laboratory)
2. Takeshi Fukuda (International Business Machines Tokyo Research Laboratory)
3. Hirofumi Matsuzawa (International Business Machines Tokyo Research Laboratory)
4. Takeshi Tokuyama (International Business Machines Tokyo Research Laboratory)
5. Kunikazu Yoda (International Business Machines Tokyo Research Laboratory)

BibTeX Citation

@article{morimoto_vldb98,
        title = {{Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases}},
        author = {Morimoto, Yasuhiko and Fukuda, Takeshi and Matsuzawa, Hirofumi and Tokuyama, Takeshi and Yoda, Kunikazu},
        journal = {PVLDB},
        series = {{VLDB} '98},
        pages = {380},
        year = {1998}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,994	RainForest - A Framework for Fast Decision Tree Construction of Large Datasets	1998	VLDB	9.3409624e-05
2,810	BOAT—Optimistic Decision Tree Construction	1999	SIGMOD	8.0985732e-05
4,092	Traversing Itemset Lattices with Statistical Metric Pruning	2000	PODS	6.907339e-05

Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 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
27	Fast Algorithms for Mining Association Rules	1994	VLDB	0.00052255472
240	An Effective Hash-Based Algorithm for Mining Association Rules	1995	SIGMOD	0.0002366381
456	Discovery of Multiple-Level Association Rules from Large Databases	1995	VLDB	0.00018124452
4,509	Constructing Efficient Decision Trees by Using Optimized Numeric Association Rules	1996	VLDB	6.6548619e-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

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5	13	Mining Association Rules between Sets of Items in Large Databases	1993	SIGMOD
6	4,509	Constructing Efficient Decision Trees by Using Optimized Numeric Association Rules	1996	VLDB
7	606	Mining Quantitative Association Rules in Large Relational Tables	1996	SIGMOD
8	558	An Efficient Algorithm for Mining Association Rules in Large Databases	1995	VLDB
9	4,565	Data Mining Using Two-Dimensional Optimized Association Rules: Scheme, Algorithms, and Visualization	1996	SIGMOD
10	4,766	Mining Optimized Association Rules for Numeric Attributes	1996	PODS