The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Mining quantitative association rules in large relational tables
Open accessAbstract
We introduce the problem of mining association rules in large relational tables containing both quantitative and categorical attributes. An example of such an association might be "10% of married people between age 50 and 60 have at least 2 cars". We deal with quantitative attributes by fine-partitioning the values of the attribute and then combining adjacent partitions as necessary. We introduce measures of partial completeness which quantify the information lost due to partitioning. A direct application of this technique can generate too many similar rules. We tackle this problem by using a "greater-than-expected-value" interest measure to identify the interesting rules in the output. We give an algorithm for mining such quantitative association rules. Finally, we describe the results of using this approach on a real-life dataset.
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Cites
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Self-citationSet-oriented mining for association rules in relational databases
Houtsma, M., & Swami, A. (2002) · Proceedings of the Eleventh International Conference on Data Engineering · doi:10.1109/icde.1995.380413 · Cited by 263
Cited by
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
Automatic subspace clustering of high dimensional data for data mining applications
Agrawal, R., Gehrke, J., Gunopulos, D., & Raghavan, P. (1998) · Proceedings of the 1998 ACM SIGMOD international conference on Management of data · doi:10.1145/276304.276314 · Cited by 2,412
Self-citationLetham, B., Rudin, C., McCormick, T. H., & Madigan, D. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas848 · Cited by 766
“For data with continuous features, a variety of procedures exist for antecedent mining [Fayyad and Irani (1993), Dougherty, Kohavi and Sahami (1995), Srikant and Agrawal (1996)].”
(2003) · Cited by 165
Metadata
- Authors
- Srikant, R., & Agrawal, R.
- Venue
- Proceedings of the 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96
- Publisher
- ACM Press
- Published
- 1996
- Pages
- 1-12
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2000473687
- Cited by
- 1,480
- References
- 2
- FWCI
- 42.79
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Srikant, R., & Agrawal, R. (1996). Mining quantitative association rules in large relational tables. Proceedings of the 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96. https://doi.org/10.1145/233269.233311
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