DBScholar

Back to papers

Mining Optimized Association Rules for Numeric Attributes

Summary: Mine rules of the form (numeric range) ⇒ Boolean, optimizing either max-support with confidence ≥ p or max-confidence with support ≥ s. Novel linear-time computational-geometry algorithms on sorted data plus randomized bucketing to avoid expensive sorts, enabling practical scalable mining across hundreds of attributes. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1087
Venue
PODS
Year
1996
Pagerank
6.5181854e-05
Overall Rank
4,766 | 67.31%
DOI
10.1145/237661.237708

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{fukuda_pods96,
        address = {New York, NY, USA},
        series = {{PODS} '96},
        title = {{Mining Optimized Association Rules for Numeric Attributes}},
        url = {https://dl.acm.org/doi/10.1145/237661.237708},
        doi = {10.1145/237661.237708},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Fukuda, Takeshi and Morimoto, Yasuhiko and Morishita, Shinichi and Tokuyama, Takeshi},
        year = {1996}
}

Incoming Citations (Sorted by Pagerank)

Showing 9 of 9 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