Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Sequence mining in categorical domains: incorporating constraints
Proceedings of the ninth international conference on Information and knowledge management
Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Mining Sequential Patterns with Regular Expression Constraints
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Drug Design by Machine Learning
Machine Intelligence 15, Intelligent Agents [St. Catherine's College, Oxford, July 1995]
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
TreeFinder: a First Step towards XML Data Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
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
Graph indexing: a frequent structure-based approach
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Efficient closed pattern mining in the presence of tough block constraints
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Chopper: efficient algorithm for tree mining
Journal of Computer Science and Technology
Incremental Mining of Frequent XML Query Patterns
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Predicting protein folding pathways
Bioinformatics
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
Faster association rules for multiple relations
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
gPrune: a constraint pushing framework for graph pattern mining
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Constraint-Based graph mining in large database
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Molecular fragment mining for drug discovery
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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In this paper, we present a general framework to mine patterns with antimonotone constraints. This framework uses a technique that structures the pattern space in a way that facilitates the integration of constraints within the mining process. Furthermore, we also introduce a powerful strategy that uses background information on the data to speed-up the mining process. We illustrate our approach on a popular structured data mining problem, the frequent subgraph mining problem, and show, through experiments on synthetic and real-life data, that this general approach has advantages over state-of-the-art pattern mining algorithms.