Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Exploiting parallelism in a structural scientific discovery system to improve scalability
Journal of the American Society for Information Science - Special topic issue: youth issues in information science
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Applying the Subdue Substructure Discovery System to the Chemical Toxicity Domain
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Discovering Frequent Geometric Subgraphs
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
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
An Efficient Algorithm for Discovering Frequent Subgraphs
IEEE Transactions on Knowledge and Data Engineering
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
The levelwise version space algorithm and its application to molecular fragment finding
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Using Data Mining to Build Integrated Discrete Event Simulations
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Frequent pattern-growth approach for document organization
Proceedings of the 2nd international workshop on Ontologies and information systems for the semantic web
Comparing graph-based representations of protein for mining purposes
Proceedings of the KDD-09 Workshop on Statistical and Relational Learning in Bioinformatics
A chorem-based approach for visually synthesizing complex phenomena
Information Visualization
WS-GraphMatching: a web service tool for graph matching
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
DESSIN: mining dense subgraph patterns in a single graph
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Semantically-guided clustering of text documents via frequent subgraphs discovery
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Using substructure mining to identify misbehavior in network provenance graphs
First International Workshop on Graph Data Management Experiences and Systems
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A majority of the existing algorithms which mine graph datasets target complete, frequent sub-graph discovery. We describe the graph-based data mining system Subdue which focuses on the discovery of sub-graphs which are not only frequent but also compress the graph dataset, using a heuristic algorithm. The rationale behind the use of a compression-based methodology for frequent pattern discovery is to produce a fewer number of highly interesting patterns than to generate a large number of patterns from which interesting patterns need to be identified. We perform an experimental comparison of Subdue with the graph mining systems gSpan and FSG on the Chemical Toxicity and the Chemical Compounds datasets that are provided with gSpan. We present results on the performance on the Subdue system on the Mutagenesis and the KDD 2003 Citation Graph dataset. An analysis of the results indicates that Subdue can efficiently discover best-compressing frequent patterns which are fewer in number but can be of higher interest.