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Efficient Mining of Frequent Subgraph in the Presence of Isomorphism
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@MISC{Huan_efficientmining,
author = {Jun Huan and Wei Wang and Jan Prins},
title = {Efficient Mining of Frequent Subgraph in the Presence of Isomorphism},
year = {}
}
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Abstract
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 graphical 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.
Keyphrases
efficient mining frequent subgraph many domain corresponding operation substantial performance gain frequent subgraph mining graph database real datasets subgraph testing active research topic novel frequent subgraph mining algorithm algebraic graphical framework redundant candidate empirical study vertical search scheme data mining community time complexity general model
