Mining Quantitative Association Rules in Large Relational Tables

Mining Quantitative Association Rules in Large Relational Tables (1996)

by Ramakrishnan Srikant , Rakesh Agrawal

Citations:

304 - 2 self

Datum	Value	Source
TITLE	Mining Quantitative Association Rules in Large Relational Tables	SVM HeaderParse 0.1
AUTHOR NAME	Ramakrishnan Srikant	SVM HeaderParse 0.1
AUTHOR ADDR	San Jose, CA 95120	SVM HeaderParse 0.1
AUTHOR NAME	Rakesh Agrawal	SVM HeaderParse 0.1
AUTHOR ADDR	San Jose, CA 95120	SVM HeaderParse 0.1
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 finepartitioning 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. 1	SVM HeaderParse 0.1
CITATIONS	12 found	ParsCit 1.0