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 frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Discovery of relational association rules
Relational Data Mining
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Mining Association Rules from Stars
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
Scalability and efficiency in multi-relational data mining
ACM SIGKDD Explorations Newsletter
Scalable Multi-Relational Association Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Multi-relational Association Rule Mining with Guidance of User
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
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This paper presents a case study of multi-relational data mining using the ConnectionBlock algorithm, applied to the database of a sugar mill. The algorithm handles multiple tables not explicitly correlated but which influence one another according to the semantics of the data involved. The experiment revealed very interesting and useful patterns that are not found using traditional algorithms. The paper aims to present how the data were prepared to obtain better expressiveness of the rules generated, showing the potential of the algorithm to find patterns in semantically related data.