Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining knowledge at multiple concept levels
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
An object-oriented approach to multi-level association rule mining
CIKM '96 Proceedings of the fifth international conference on Information and knowledge management
Hierarchy-based mining of association rules in data warehouses
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Discovery of multi-level rules and exceptions from a distributed database
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining needle in a haystack: classifying rare classes via two-phase rule induction
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Data Mining Query Language for Object-0riented Database
ADBIS '98 Proceedings of the Second East European Symposium on Advances in Databases and Information Systems
Data Mining
Rule quality for multiple-rule classifier: Empirical expertise and theoretical methodology
Intelligent Data Analysis
Mining weighted association rules
Intelligent Data Analysis
Rule mining with prior knowledge -- a belief networks approach
Intelligent Data Analysis
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This paper describes the use of a concept hierarchy for improving the results of association rule mining. Given a large set of tuples with demographic information and personal interest information, association rules can be derived, that associate ages and gender with interests. However, it is a problem to come up with rules with high support whenever the mined data set is sparse. On the other hand, if rules with high support can be generated, they tend to involve interests that are too abstract to be of practical use. To overcome the first problem, we have developed a method of raising data instances to higher levels in the ontology. In this paper we give a formal definition of the raising operation. We also show that in some cases data mining with raised data leads to rules that better represent the reality. In order to avoid the second problem, namely rules that are too abstract, we formulate a notion of an optimal target level for the raising operation. We then derive two estimates for this optimal raising level. Knowing to which level to raise reduces the computational effort of raising to several levels and reduces the user effort of selecting those mined rules that best fit her/his needs from a large candidate set.