Computing frequent graph patterns from semistructured data

N. Vanetik, E. Gudes, S. E. Shimony

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

108 Scopus citations

Abstract

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. We study the problem of discovering typical patterns of graph data. The discovered patterns can be useful for many applications, including: compact representation of source information and a road-map for browsing and querying information sources. Difficulties arise in the discovery task from the complexity of some of the required sub-tasks, such as sub-graph isomorphism. This paper proposes a new algorithm for mining graph data, based on a novel definition of support. Empirical evidence shows practical, as well as theoretical, advantages of our approach.

Original languageEnglish
Title of host publicationProceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002
Pages458-465
Number of pages8
StatePublished - 1 Dec 2002
Event2nd IEEE International Conference on Data Mining, ICDM '02 - Maebashi, Japan
Duration: 9 Dec 200212 Dec 2002

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference2nd IEEE International Conference on Data Mining, ICDM '02
Country/TerritoryJapan
CityMaebashi
Period9/12/0212/12/02

ASJC Scopus subject areas

  • Engineering (all)

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