Transversing itemset lattices with statistical metric pruning
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent subgraph mining in outerplanar graphs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
ORIGAMI: Mining Representative Orthogonal Graph Patterns
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Don't be afraid of simpler patterns
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
A quantitative comparison of the subgraph miners mofa, gspan, FFSM, and gaston
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Frequent subgraph mining in outerplanar graphs
Data Mining and Knowledge Discovery
Latent structure pattern mining
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
TGP: mining top-K frequent closed graph pattern without minimum support
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Interactive discriminative mining of chemical fragments
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Annals of Mathematics and Artificial Intelligence
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We present a new approach to large-scale graph mining based on so-called backbone refinement classes. The method efficiently mines tree-shaped subgraph descriptors under minimum frequency and significance constraints, using classes of fragments to reduce feature set size and running times. The classes are defined in terms of fragments sharing a common backbone. The method is able to optimize structural inter-feature entropy as opposed to occurrences, which is characteristic for open or closed fragment mining. In the experiments, the proposed method reduces feature set sizes by 90 % and 30 % compared to complete tree mining and open tree mining, respectively. Evaluation using crossvalidation runs shows that their classification accuracy is similar to the complete set of trees but significantly better than that of open trees. Compared to open or closed fragment mining, a large part of the search space can be pruned due to an improved statistical constraint (dynamic upper bound adjustment), which is also confirmed in the experiments in lower running times compared to ordinary (static) upper bound pruning. Further analysis using large-scale datasets yields insight into important properties of the proposed descriptors, such as the dataset coverage and the class size represented by each descriptor. A final cross-validation run confirms that the novel descriptors render large training sets feasible which previously might have been intractable.