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
Parallel algorithms for mining frequent structural motifs in scientific data
Proceedings of the 18th annual international conference on Supercomputing
Eigenspace-based anomaly detection in computer systems
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery from Transportation Network Data
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Finding Patterns on Protein Surfaces: Algorithms and Applications to Protein Classification
IEEE Transactions on Knowledge and Data Engineering
Comparison of graph-based and logic-based multi-relational data mining
ACM SIGKDD Explorations Newsletter
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
Subdue: compression-based frequent pattern discovery in graph data
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Time and space efficient discovery of maximal geometric graphs
DS'07 Proceedings of the 10th international conference on Discovery science
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
MARGIN: Maximal frequent subgraph mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Implicit enumeration of patterns
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
Efficient geometric graph matching using vertex embedding
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A new proposal for graph classification using frequent geometric subgraphs
Data & Knowledge Engineering
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As data mining techniques are being increasingly appliedto non-traditional domains, existing approaches forfinding frequent itemsets cannot be used as they cannotmodel the requirement of these domains. An alternate wayof modeling the objects in these data sets, is to use a graphto model the database objects. Within that model, the problemof finding frequent patterns becomes that of discoveringsubgraphs that occur frequently over the entire set ofgraphs. In this paper we present a computationally efficientalgorithm for finding frequent geometric subgraphs ina large collection of geometric graphs. Our algorithm isable to discover geometric subgraphs that can be rotation,scaling and translation invariant, and it can accommodateinherent errors on the coordinates of the vertices. Our experimentalresults show that our algorithms requires relativelylittle time, can accommodate low support values, andscales linearly on the number of transactions.