Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on 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
Application of ART2 Networks and Self-Organizing Maps to Collaborative Filtering
Revised Papers from the nternational Workshops OHS-7, SC-3, and AH-3 on Hypermedia: Openness, Structural Awareness, and Adaptivity
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
GO-SPADE: mining sequential patterns over datasets with consecutive repetitions
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
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Relationships between objects constitute an important component of domain knowledge and represent an added value to data and knowledge discovery and management. Some existing studies in data mining (including association rule mining) exploit domain knowledge to better conduct tasks in applications such as e-learning and e-commerce. However, general semantic links between objects are either not exploited in the mining process or limited to is-a relations. In this paper we present the basis of a new approach where direct and indirect domain relations between items (basket market analysis) are used to reveal hidden knowledge in item transactions. First, we define the notion of relation association rule as an association that holds between sequences of relations. Such a type of rules is produced from item transactions and existing links among items in a given domain ontology. Then, we define an a priori-based algorithm for relation association rule mining. Finally, we illustrate the potential of our approach for recommendation purposes.