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Extracting frequent connected subgraphs from large graph sets
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Proceedings of the 2005 ACM SIGMOD international conference on Management of data
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Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Mining tree queries in a graph
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On mining cross-graph quasi-cliques
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Discovering frequent topological structures from graph datasets
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Key semantics extraction by dependency tree mining
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A pattern mining method for interpretation of interaction
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Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
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eMailSift: Email Classification Based on Structure and Content
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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A characterization of data mining algorithms on a modern processor
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Frequency-based views to pattern collections
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Graph-based Relational Learning with Application to Security
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VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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DB-FSG: An SQL-Based Approach for Frequent Subgraph Mining
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Identifying bug signatures using discriminative graph mining
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Mining globally distributed frequent subgraphs in a single labeled graph
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HDB-Subdue: A Scalable Approach to Graph Mining
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Frequent subgraph pattern mining on uncertain graph data
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Extracting common design patterns from a set of solid models
Computer-Aided Design
Comparing stars: on approximating graph edit distance
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Output space sampling for graph patterns
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Mining graph patterns efficiently via randomized summaries
Proceedings of the VLDB Endowment
Mining frequent closed patterns in pointset databases
Information Systems
Frequency-based views to pattern collections
Discrete Applied Mathematics - Special issue: Discrete mathematics & data mining II (DM & DM II)
The Gaston Tool for Frequent Subgraph Mining
Electronic Notes in Theoretical Computer Science (ENTCS)
Inferring Graph Grammars by Detecting Overlap in Frequent Subgraphs
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A method of improving the efficiency of mining sub-structures in molecular structure databases
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Mining closed frequent free trees in graph databases
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Discovery of frequent graph patterns that consist of the vertices with the complex structures
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Pruthak: mining and analyzing graph substructures
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MARGIN: Maximal frequent subgraph mining
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A new algorithm for mining frequent connected subgraphs based on adjacency matrices
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Graph-based Relational Learning with Application to Security
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The complexity of mining maximal frequent subgraphs
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Frequent subgraph summarization with error control
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A new proposal for graph classification using frequent geometric subgraphs
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Mining frequent correlated graphs with a new measure
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As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of these domains. An alternate way of modeling the objects in these data sets is to use graphs. Within that model, the problem of finding frequent patterns becomes that of discovering subgraphs that occur frequently over the entire set of graphs. In this paper we present a computationally efficient algorithm for finding all frequent subgraphs in large graph databases. We evaluated the performance of the algorithm by experiments with synthetic datasets as well as a chemical compound dataset. The empirical results show that our algorithm scales linearly with the number of input transactions and it is able to discover frequent subgraphs from a set of graph transactions reasonably fast, even though we have to deal with computationally hard problems such as canonical labeling of graphs and subgraph isomorphism which are not necessary for traditional frequent itemset discovery.