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: h0bb032150269bd8a
Venue: VLDB
Year: 1998
Pagerank: 6.2516885e-05
Overall Rank: 5,154 | 65.35%
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
2,031	RainForest - A Framework for Fast Decision Tree Construction of Large Datasets	1998	VLDB	9.1572841e-05
2,870	BOAT—Optimistic Decision Tree Construction	1999	SIGMOD	7.9233098e-05
4,180	Traversing Itemset Lattices with Statistical Metric Pruning	2000	PODS	6.7554731e-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	0.00064420972
29	Fast Algorithms for Mining Association Rules	1994	VLDB	0.0005121339
250	An Effective Hash-Based Algorithm for Mining Association Rules	1995	SIGMOD	0.00023156569
467	Discovery of Multiple-Level Association Rules from Large Databases	1995	VLDB	0.00017766906
4,600	Constructing Efficient Decision Trees by Using Optimized Numeric Association Rules	1996	VLDB	6.507687e-05
4,664	Data Mining Using Two-Dimensional Optimized Association Rules: Scheme, Algorithms, and Visualization	1996	SIGMOD	6.4800763e-05
4,873	Mining Optimized Association Rules for Numeric Attributes	1996	PODS	6.372839e-05

Semantically Similar Papers

#	Overall Rank	Paper	Year	Venue
1	1,679	Fast Algorithms for Projected Clustering	1999	SIGMOD
2	7,965	Efficient Approximate Algorithms for Empirical Entropy and Mutual Information	2021	SIGMOD
3	473	Sampling Large Databases for Association Rules	1996	VLDB
4	4,637	Association Rules over Interval Data	1997	SIGMOD
5	13	Mining Association Rules between Sets of Items in Large Databases	1993	SIGMOD
6	4,600	Constructing Efficient Decision Trees by Using Optimized Numeric Association Rules	1996	VLDB
7	620	Mining Quantitative Association Rules in Large Relational Tables	1996	SIGMOD
8	573	An Efficient Algorithm for Mining Association Rules in Large Databases	1995	VLDB
9	4,664	Data Mining Using Two-Dimensional Optimized Association Rules: Scheme, Algorithms, and Visualization	1996	SIGMOD
10	4,873	Mining Optimized Association Rules for Numeric Attributes	1996	PODS