Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 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
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Data Mining and Knowledge Discovery
Pattern Mining in Frequent Dynamic Subgraphs
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Correlation search in graph databases
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient mining of frequent sequence generators
Proceedings of the 17th international conference on World Wide Web
Discovery of Internal and External Hyperclique Patterns in Complex Graph Databases
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
A Fast Method to Mine Frequent Subsequences from Graph Sequence Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Mining correlated subgraphs in graph databases
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Dynamic graphs or a sequence of graphs attract much attention recently. In this paper, as a first step towards finding significant patterns hidden in dynamic graphs, we consider the problem of mining successive sequence of subgraphs which appear frequently in a long sequence of graphs. In addition, to exclude insignificant patterns, we take into account the mutual dependency measured by *** -correlation coefficient among the components in patterns. An algorithm named CorSSS, which utilizes the generality ordering of patterns effectively, is developed for enumerating all frequent and correlated patterns. The effectiveness of CorSSS, is confirmed through the experiments using real datasets.