Mining Complex Data

Volume 165 of the series Studies in Computational Intelligence pp 189-205

Full Perfect Extension Pruning for Frequent Subgraph Mining

  • Christian BorgeltAffiliated withEuropean Center for Soft Computing
  • , Thorsten MeinlAffiliated withNycomed Chair for Bioinformatics and Information Mining Dept. of Computer and Information Science, University of Konstanz

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Mining graph databases for frequent subgraphs has recently developed into an area of intensive research. Its main goals are to reduce the execution time of the existing basic algorithms and to enhance their capability to find meaningful graph fragments. Here we present a method to achieve the former, namely an improvement of what we called “perfect extension pruning” in an earlier paper [4]. With this method the number of generated fragments and visited search tree nodes can be reduced, often considerably, thus accelerating the search.We describe the method in detail and present experimental results that demonstrate its usefulness.