CloseGraph: mining closed frequent graph patterns. Recent research on pattern discovery has progressed form mining frequent itemsets and sequences to mining structured patterns including trees, lattices, and graphs. As a general data structure, graph can model complicated relations among data with wide applications in bioinformatics, Web exploration, and etc. However, mining large graph patterns in challenging due to the presence of an exponential number of frequent subgraphs. Instead of mining all the subgraphs, we propose to mine closed frequent graph patterns. A graph g is closed in a database if there exists no proper supergraph of g that has the same support as g. A closed graph pattern mining algorithm, CloseGraph, is developed by exploring several interesting pruning methods. Our performance study shows that CloseGraph not only dramatically reduces unnecessary subgraphs to be generated but also substantially increases the efficiency of mining, especially in the presence of large graph patterns

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  1. Ferré, Sébastien; Cellier, Peggy: Graph-FCA: an extension of formal concept analysis to knowledge graphs (2020)
  2. Meng, Jinghan; Pitaksirianan, Napath; Tu, Yi-Cheng: Counting frequent patterns in large labeled graphs: a hypergraph-based approach (2020)
  3. Zhang, Richong; Mao, Yongyi; Zhao, Weihua: Knowledge graphs completion via probabilistic reasoning (2020)
  4. Rehman, Saif Ur; Asghar, Sohail; Zhuang, Yan; Fong, Simon: Performance evaluation of frequent subgraph discovery techniques (2014)
  5. Spyropoulou, Eirini; De Bie, Tijl; Boley, Mario: Interesting pattern mining in multi-relational data (2014)
  6. Garriga, Gemma C.; Khardon, Roni; De Raedt, Luc: Mining closed patterns in relational, graph and network data (2013)
  7. Kibriya, Ashraf M.; Ramon, Jan: Nearly exact mining of frequent trees in large networks (2013)
  8. Livi, Lorenzo; Rizzi, Antonello: The graph matching problem (2013)
  9. Shelokar, Prakash; Quirin, Arnaud; Cordón, Óscar: A multiobjective evolutionary programming framework for graph-based data mining (2013) ioport
  10. Kim, Chulyun; Miao, Hui; Shim, Kyuseok: CATCH: a detecting algorithm for coalition attacks of hit inflation in internet advertising (2011) ioport
  11. Maunz, Andreas; Helma, Christoph; Kramer, Stefan: Efficient mining for structurally diverse subgraph patterns in large molecular databases (2011)
  12. Sim, Kelvin; Liu, Guimei; Gopalkrishnan, Vivekanand; Li, Jinyan: A case study on financial ratios via cross-graph quasi-bicliques (2011) ioport
  13. Takigawa, Ichigaku; Mamitsuka, Hiroshi: Efficiently mining (\delta)-tolerance closed frequent subgraphs (2011)
  14. Zhang, Shuo; Gao, Xiaofeng; Wu, Weili; Li, Jianzhong; Gao, Hong: Efficient algorithms for supergraph query processing on graph databases (2011)
  15. Zhu, Linhong; Ng, Wee Keong; Cheng, James: Structure and attribute index for approximate graph matching in large graphs (2011) ioport
  16. Aggarwal, Charu C.; Zhao, Yuchen; Yu, Philip S.: A framework for clustering massive graph streams (2010)
  17. Balcázar, José L.; Bifet, Albert; Lozano, Antoni: Mining frequent closed rooted trees (2010)
  18. Chaoji, Vineet; Hasan, Mohammad Al; Salem, Saeed; Besson, Jeremy; Zaki, Mohammed J.: ORIGAMI: a novel and effective approach for mining representative orthogonal graph patterns (2008)
  19. Chaoji, Vineet; Hasan, Mohammad Al; Salem, Saeed; Zaki, Mohammed J.: An integrated, generic approach to pattern mining: data mining template library (2008) ioport
  20. Zhao, Peixiang; Yu, Jeffrey Xu: Fast frequent free tree mining in graph databases (2008) ioport

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