ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Mining Molecular Fragments: Finding Relevant Substructures of Molecules
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
SPIN: mining maximal frequent subgraphs from graph databases
Proceedings of the tenth 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
\delta-Tolerance Closed Frequent Itemsets
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
MARGIN: Maximal Frequent Subgraph Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
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A major challenge in frequent subgraph mining is the sheer size of its mining results. In many cases, a low minimum support may generate an explosive number of frequent subgraphs, which severely restricts the usage of frequent subgraph mining. In this paper, we study a new problem of mining frequent jump patterns from graph databases. Mining frequent jump patterns can dramatically reduce the number of output graph patterns, and still capture interesting graph patterns. By integrating the operation of checking jump patterns into the well-known DFS code tree enumeration framework, we present an efficient algorithm JPMiner for this new problem. We experimentally evaluate various aspects of JPMiner using both real and synthetic datasets. Experimental results demonstrate that the number of frequent jump patterns is much smaller than that of closed frequent graph patterns, and JPMiner is efficient and scalable in mining frequent jump patterns.