Mining Quantitative Association Rules in Large Relational Tables

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Mining Quantitative Association Rules in Large Relational Tables
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 nepartitioning 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.
Ramakrishnan Srikant, Rakesh Agrawal
Added 08 Aug 2010
Updated 08 Aug 2010
Type Conference
Year 1996
Authors Ramakrishnan Srikant, Rakesh Agrawal
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