Model matching in robot vision by subgraph isomorphism
Pattern Recognition
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
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
Proceedings of the 27th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Frequent Geometric Subgraphs
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Computing Frequent Graph Patterns from Semistructured Data
ICDM '02 Proceedings of the 2002 IEEE International Conference on 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
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
Mining Edge-Weighted Call Graphs to Localise Software Bugs
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
RankClus: integrating clustering with ranking for heterogeneous information network analysis
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
P-Rank: a comprehensive structural similarity measure over information networks
Proceedings of the 18th ACM conference on Information and knowledge management
Mining heterogeneous information networks: a structural analysis approach
ACM SIGKDD Explorations Newsletter
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Transportation and Logistics are a major sector of the economy, however data analysis in this domain has remained largely in the province of optimization. The potential of data mining and knowledge discovery techniques is largely untapped. Transportation networks are naturally represented as graphs. This paper explores the problems in mining of transportation network graphs: We hope to find how current techniques both succeed and fail on this problem, and from the failures, we hope to present new challenges for data mining. Experimental results from applying both existing graph mining and conventional data mining techniques to real transportation network data are provided, including new approaches to making these techniques applicable to the problems. Reasons why these techniques are not appropriate are discussed. We also suggest several challenging problems to precipitate research and galvanize future work in this area.