Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Mining Association Rules from Stars
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining itemsets in the presence of missing values
Proceedings of the 2007 ACM symposium on Applied computing
CloseViz: visualizing useful patterns
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
A framework for mining interesting pattern sets
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
A framework for mining interesting pattern sets
ACM SIGKDD Explorations Newsletter
Frequent itemset mining of uncertain data streams using the damped window model
Proceedings of the 2011 ACM Symposium on Applied Computing
Semantic knowledge discovery from heterogeneous data sources
EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
Granular association rule mining through parametric rough sets
BI'12 Proceedings of the 2012 international conference on Brain Informatics
Mining frequent conjunctive queries using functional and inclusion dependencies
The VLDB Journal — The International Journal on Very Large Data Bases
Interesting pattern mining in multi-relational data
Data Mining and Knowledge Discovery
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In this paper we propose a new and elegant approach toward the generalization of frequent itemset mining to the multi-relational case. We define relational itemsets that contain items from several relations, and a support measure that can easily be interpreted based on the key dependencies as defined in the relational scheme. We present an efficient depth-first algorithm, which mines relational itemsets directly from arbitrary relational databases. Several experiments show the practicality and usefulness of the proposed approach.