Association rule selection in a data mining environment

Abstract

Data mining methods easily produce large collections of rules, so that the usability of the methods is hampered by the sheer size of the rule set. One way of limiting the size of the result set is to provide the user with tools to help in finding the truly interesting rules. We use this approach in a case study where we search for association rules in NCHS health care data, and select interesting subsets of the result by using a simple query language implemented in the KESO data mining system. Our results emphasize the importance of the explorative approach supported by efficient selection tools.