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Published Articles >> Table of Contents >> Abstract
Third IEEE International Conference on Data Mining (ICDM'03)
p. 549
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
Jun Huan, University of North Carolina, Chapel Hill
Wei Wang, University of North Carolina, Chapel Hill
Jan Prins, University of North Carolina, Chapel Hill
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250974
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| Abstract |
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Frequent subgraph mining is an active research topic in the data mining community. A graph is a general model to represent data and has been used in many domains like cheminformatics and bioinformatics. Mining patterns from graph databases is challenging since graph related operations, such as subgraph testing, generally have higher time complexity than the corresponding operations on itemsets, sequences, and trees, which have been studied extensively. In this paper, we propose a novel frequent subgraph mining algorithm: FFSM, which employs a vertical search scheme within an algebraic graph framework we have developed to reduce the number of redundant candidates proposed. Our empirical study on synthetic and real datasets demonstrates that FFSM achieves a substantial performance gain over the current start-of-the-art subgraph mining algorithm gSpan.
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Citation:
Jun Huan, Wei Wang, Jan Prins,
"Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism,"
icdm,
p. 549,
Third IEEE International Conference on Data Mining (ICDM'03),
2003
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