gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Minimal Distinguishing Subsequence Patterns with Gap Constraints
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
CLAN: An Algorithm for Mining Closed Cliques from Large Dense Graph Databases
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Out-of-core coherent closed quasi-clique mining from large dense graph databases
ACM Transactions on Database Systems (TODS)
Efficient Mining of Contrast Patterns and Their Applications to Classification
ICISIP '05 Proceedings of the 2005 3rd International Conference on Intelligent Sensing and Information Processing
Efficient mining of top-k breaker emerging subgraph patterns from graph datasets
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
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Distinguishing patterns represent strong distinguishing knowledge and are very useful for constructing powerful, accurate and robust classifiers. The distinguishing graph patterns(DGPs) are able to capture structure differences between any two categories of graph datasets. Whereas, few previous studies worked on the discovery of DGPs. In this paper, as the first, we study the problem of mining the complete set of minimal DGPs with any number of positive graphs, arbitrary positive support and negative support. We proposed a novel algorithm, MDGP-Mine, to discover the complete set of minimal DGPs. The empirical results show that MDGP-Mine is efficient and scalable.