Efficient top-down induction of logic programs
ACM SIGART Bulletin
Handbook of graph grammars and computing by graph transformation: vol. 2: applications, languages, and tools
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Relational Data Mining
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
IEEE Intelligent Systems
Graph-Based Relational Concept Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Combining Statistical and Relational Methods for Learning in Hypertext Domains
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Graph-based hierarchical conceptual clustering
The Journal of Machine Learning Research
Biological applications of multi-relational data mining
ACM SIGKDD Explorations Newsletter
Graph-based relational learning: current and future directions
ACM SIGKDD Explorations Newsletter
A Process Catalog for Workflow Generation
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Concept-based learning of human behavior for customer relationship management
Information Sciences: an International Journal
Detecting anomalies in cargo using graph properties
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
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We describe an approach to learning patterns in relational data represented as a graph. The approach, implemented in the Subdue system, searches for patterns that maximally compress the input graph. Subdue can be used for supervised learning, as well as unsupervised pattern discovery and clustering. We apply Subdue in domains related to homeland security and social network analysis.