CloseGraph: Mining closed frequent graph patterns

Xifeng Yan, Jiawei Han

Research output: Contribution to conferencePaperpeer-review


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.

Original languageEnglish (US)
Number of pages10
StatePublished - 2003
Event9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03 - Washington, DC, United States
Duration: Aug 24 2003Aug 27 2003


Other9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03
Country/TerritoryUnited States
CityWashington, DC


  • Canonical label
  • Closed pattern
  • Frequent graph
  • Graph representation

ASJC Scopus subject areas

  • Software
  • Information Systems


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