DBScholar

Back to papers

Data Mining Using Two-Dimensional Optimized Association Rules: Scheme, Algorithms, and Visualization

Summary: Two-dimensional optimized association rules over two numeric attributes and one Boolean attribute (e.g., Age, Balance ⇒ CardLoan=Yes). Algorithms find optimal regions for gain, support, and confidence under rectangle and admissible (connected, x-monotone) region classes; implemented for admissible regions with a visualization system. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
2929
Venue
SIGMOD
Year
1996
Pagerank
6.6278063e-05
Overall Rank
4,565 | 68.69%
DOI
10.1145/233269.233313

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{fukuda_sigmod96,
        title = {{Data Mining Using Two-Dimensional Optimized Association Rules: Scheme, Algorithms, and Visualization}},
        author = {Fukuda, Takeshi and Morimoto, Yasuhiko and Morishita, Shinichi and Tokuyama, Takeshi},
        series = {{SIGMOD} '96},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/233269.233313},
        url = {https://dl.acm.org/doi/10.1145/233269.233313},
        year = {1996}
}

Incoming Citations (Sorted by Pagerank)

Showing 11 of 11 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 cited papers.

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

Previous Page 1 / 1 Next

Semantically Similar Papers