Mining quantitative association rules in large relational tables
- 1 June 1996
- journal article
- conference paper
- Published by Association for Computing Machinery (ACM) in ACM SIGMOD Record
- Vol. 25 (2), 1-12
- https://doi.org/10.1145/235968.233311
Abstract
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.Keywords
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