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 quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Object Exchange Across Heterogeneous Information Sources
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Don't Scrap It, Wrap It! A Wrapper Architecture for Legacy Data Sources
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Object Fusion in Mediator Systems
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
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We examined the problem of applying association rules in a heterogeneous database. Due to heterogeneity, we have to generalize the notion of association rule to define a new heterogeneous association rule (h-rule) which denotes data association between various types of data in different subsystems of a heterogeneous and multimedia database, such as music pieces vs. photo pictures, etc. Boolean association rule and quantitative association rule are special cases of h-rule. H-rule integrates previously defined rule concepts and expands association rule mining from single dataset mining to database mining.