Discovering frequent geometric subgraphs

Michihiro Kuramochi, George Karypis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Scopus citations


As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of these domains. An alternate way of modeling the objects in these data sets, is to use a graph to model the database objects. Within that model, the problem of finding frequent patterns becomes that of discovering subgraphs that occur frequently over the entire set of graphs. In this paper we present a computationally efficient algorithm for finding frequent geometric subgraphs in a large collection of geometric graphs. Our algorithm is able to discover geometric subgraphs that can be rotation, scaling and translation invariant, and it can accommodate inherent errors on the coordinates of the vertices. Our experimental results show that our algorithms requires relatively little time, can accommodate low support values, and scales linearly on the number of transactions.

Original languageEnglish (US)
Title of host publicationProceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002
Number of pages8
StatePublished - 2002
Event2nd IEEE International Conference on Data Mining, ICDM '02 - Maebashi, Japan
Duration: Dec 9 2002Dec 12 2002

Publication series

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


Other2nd IEEE International Conference on Data Mining, ICDM '02

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