Elements of information theory
Elements of information theory
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third 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
SPIN: mining maximal frequent subgraphs from graph databases
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Generalized Substructures from a Set of Labeled Graphs
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Gene Ontology-Based Annotation Analysis and Categorization of Metabolic Pathways
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
Mining biological networks for unknown pathways
Bioinformatics
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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
Taxonomy-driven lumping for sequence mining
Data Mining and Knowledge Discovery
A framework for mining meaningful usage patterns within a semantically enhanced web portal
Proceedings of the Third C* Conference on Computer Science and Software Engineering
A general framework to encode heterogeneous information sources for contextual pattern mining
Proceedings of the 21st ACM international conference on Information and knowledge management
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New graph structures where node labels are members of hierarchically organized ontologies or taxonomies have become commonplace in different domains, e.g., life sciences. It is a challenging task to mine for frequent patterns in this new graph model which we call taxonomy-superimposed graphs, as there may be many patterns that are implied by the generalization/specialization hierarchy of the associated node label taxonomy. Hence, 'standard' graph mining techniques are not directly applicable. In this paper, we present Taxogram, a taxonomy-superimposed graph mining algorithm that can efficiently discover frequent graph structures in a database of taxonomy-superimposed graphs. Taxogram has two advantages: (i) It performs a subgraph isomorphism test once per class of patterns which are structurally isomorphic, but have different labels, and (ii) it reconciles standard graph mining methods with taxonomy-based graph mining and takes advantage of well-studied methods in the literature. Taxogram has three stages: (a) relabeling nodes in the input database, (b) mining pattern classes/families and constructing associated occurrence indices, and (c) computing patterns and eliminating useless (i.e., over-generalized) patterns by post-processing occurrence indices. Experimental results show that Taxogram is significantly more efficient and more scalable compared to other alternative approaches.