Efficient Mining of Intertransaction Association Rules
IEEE Transactions on Knowledge and Data Engineering
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
Cross-relational clustering with user's guidance
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Unified Modeling Language User Guide, The (2nd Edition) (Addison-Wesley Object Technology Series)
Unified Modeling Language User Guide, The (2nd Edition) (Addison-Wesley Object Technology Series)
Efficiently Mining Frequent Embedded Unordered Trees
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
An efficient algorithm for mining frequent inter-transaction patterns
Information Sciences: an International Journal
Mining induced and embedded subtrees in ordered, unordered, and partially-ordered trees
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Interestingness measures for association rules within groups
Intelligent Data Analysis
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This paper proposes a new approach to mine multirelational databases. Our approach is based on the representation of a multirelational database as a set of trees. Tree mining techniques can then be applied to identify frequent patterns in this kind of databases. We propose two alternative schemes for representing a multirelational database as a set of trees. The frequent patterns that can be identified in such set of trees can be used as the basis for other multirelational data mining techniques, such as association rules, classification, or clustering.