Mining frequent labeled and partially labeled graph patterns

N. Vanetik, E. Gudes

Research output: Contribution to conferencePaperpeer-review

16 Scopus citations


Whereas data mining in structured data focuses on frequent data values, in semi-structured and graph data the emphasis is on frequent labels and common topologies. Here, the structure of the data is just as important as its content. When data contains large amount of different labels, both fully labeled and partially labeled data may be useful. More informative patterns can be found in the database if some of the pattern nodes can be regarded as 'unlabeled'. We study the problem of discovering typical fully and partially labeled patterns of graph data. Discovered patterns are useful in many applications, including: compact representation of source information and a road-map for browsing and querying information sources.

Original languageEnglish
Number of pages12
StatePublished - 1 Jun 2004
Externally publishedYes
EventProceedings - 20th International Conference on Data Engineering - ICDE 2004 - Boston, MA., United States
Duration: 30 Mar 20042 Apr 2004


ConferenceProceedings - 20th International Conference on Data Engineering - ICDE 2004
Country/TerritoryUnited States
CityBoston, MA.

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems


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