Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Mining with Cover and Extension Operators
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Discovery of Representative Association Rules
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Representative Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Inferring Knowledge from Frequent Patterns
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
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An important data mining problem is to restrict the number of association rules to those that are novel, interesting, useful. However, there are situations when a user is not allowed to access the database and can deal only with the rules provided by somebody else. The number of rules can be limited e.g. for security reasons or the rules are of low quality. Still, the user hopes to find new interesting relationships. In this paper we propose how to induce as much knowledge as possible from the provided set of rules. The algorithms for inducing theory as well as for computing maximal covering rules for the theory are provided. In addition, we show how to test the consistency of rules and how to extract a consistent subset of rules.