CLIP: concept learning from inference patterns
Artificial Intelligence - Special issue: AI research in Japan
Ordered and Unordered Tree Inclusion
SIAM Journal on Computing
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SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Molecular feature mining in HIV data
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Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
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ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Efficiently mining frequent trees in a forest
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CloseGraph: mining closed frequent graph patterns
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IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
The levelwise version space algorithm and its application to molecular fragment finding
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Faster association rules for multiple relations
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GrAF: a graph-based format for linguistic annotations
LAW '07 Proceedings of the Linguistic Annotation Workshop
Pattern discovery from graph-structured data: a data mining perspective
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Patterns discovery for efficient structured probabilistic inference
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
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AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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The derivation of frequent subgraphs from a dataset of labeled graphs has high computational complexity because the hard problems of isomorphism and subgraph isomorphism have to be solved as part of this derivation. To deal with this computational complexity, all previous approaches have focused on one particular kind of graph. In this paper, we propose an approach to conduct a complete search for various classes of frequent subgraphs in a massive dataset of labeled graphs within a practical time. The power of our approach comes from the algebraic representation of graphs, its associated operations and well-organized bias constraints to limit the search space efficiently. The performance has been evaluated using real world datasets, and the high scalability and flexibility of our approach have been confirmed with respect to the amount of data and the computation time.