Efficient Frequent Query Discovery in FARMER

@inproceedings{Nijssen2003EfficientFQ,
  title={Efficient Frequent Query Discovery in FARMER},
  author={Siegfried Nijssen and Joost N. Kok},
  booktitle={PKDD},
  year={2003}
}
The upgrade of frequent item set mining to a setup with multiple relations – frequent query mining – poses many efficiency problems. Taking Object Identity as starting point, we present several optimization techniques for frequent query mining algorithms. The resulting algorithm has a better performance than a previous ILP algorithm and competes with more specialized graph mining algorithms in performance. 

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References

Publications referenced by this paper.
SHOWING 1-9 OF 9 REFERENCES

Improving the Efficiency of Inductive Logic Programming Through the Use of Query Packs

VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

An Efficient Algorithm for Discovering Frequent Subgraphs

  • M. Kuramochi, G. Karypsis
  • Technical Report 02-026, University of Minesota.
  • 2002
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

gSpan: graph-based substructure pattern mining

  • Xifeng Yan, Jiawei Han
  • Computer Science
  • 2002 IEEE International Conference on Data Mining, 2002. Proceedings.
  • 2002
VIEW 11 EXCERPTS
HIGHLY INFLUENTIAL

Frequent subgraph discovery

VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

Fast Discovery of Association Rules

VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Discovery of frequent DATALOG patterns

VIEW 2 EXCERPTS