Clustering graphs by weighted substructure mining
ICML '06 Proceedings of the 23rd international conference on Machine learning
Mining Generalized Associations of Semantic Relations from Textual Web Content
IEEE Transactions on Knowledge and Data Engineering
Taxonomy-superimposed graph mining
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Partial least squares regression for graph mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Independent informative subgraph mining for graph information retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
Mining relationship associations from knowledge about failures using ontology and inference
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Latent structure pattern mining
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Mining RDF metadata for generalized association rules
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Using and learning semantics in frequent subgraph mining
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
A general framework to encode heterogeneous information sources for contextual pattern mining
Proceedings of the 21st ACM international conference on Information and knowledge management
International Journal of Knowledge Discovery in Bioinformatics
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The problem of mining frequent itemsets in transactional data has been studied frequently and has yielded several algorithms that can find the itemsets within a limited amount of time. Some of them can derive "generalized" frequent itemsets consisting of items at any level of a taxonomy. Recently, several approaches have been proposed to mine frequent substructures (patterns) from a set of labeled graphs. The graph mining approaches are easily extended to mine generalized patterns where some vertices and/or edges have labels at any level of a taxonomy of the labels by extending the definition of "subgraph". However, the extended method outputs a massive set of the patterns most of which are over-generalized, which causes computation explosion. In this paper, an efficient and novel method is proposed to discover all frequent patterns which are not over-generalized from labeled graphs, when taxonomies on vertex and edge labels are available.