Mining quantitative association rules in large relational tables

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Mining quantitative association rules in large relational tables

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International Conference on Management of Data archive
Proceedings of the 1996 ACM SIGMOD international conference on Management of data table of contents

Montreal, Quebec, Canada

Pages: 1 - 12

Year of Publication: 1996

ISBN:0-89791-794-4

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Authors

Ramakrishnan Srikant	IBM Almaden Research Center, 650 Harry Road, San Jose, CA and Department of Computer Science, University of Wisconsin, Madison
Rakesh Agrawal	IBM Almaden Research Center, 650 Harry Road, San Jose, CA

Sponsors

SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory

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ACM New York, NY, USA

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Downloads (6 Weeks): 24, Downloads (12 Months): 185, Citation Count: 208

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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.

REFERENCES

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