Efficiently mining δ-tolerance closed frequent subgraphs

@article{Takigawa2010EfficientlyM,
  title={Efficiently mining δ-tolerance closed frequent subgraphs},
  author={Ichigaku Takigawa and Hiroshi Mamitsuka},
  journal={Machine Learning},
  year={2010},
  volume={82},
  pages={95-121}
}
The output of frequent pattern mining is a huge number of frequent patterns, which are very redundant, causing a serious problem in understandability. We focus on mining frequent subgraphs for which well-considered approaches to reduce the redundancy are limited because of the complex nature of graphs. Two known, standard solutions are closed and maximal frequent subgraphs, but closed frequent subgraphs are still redundant and maximal frequent subgraphs are too specific. A more promising… CONTINUE READING
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