Similarity Search in Structured Data

  • Hans-Peter Kriegel
  • Stefan Schönauer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2737)


Recently, structured data is getting more and more important in database applications, such as molecular biology, image retrieval or XML document retrieval. Attributed graphs are a natural model for the structured data in those applications. For the clustering and classification of such structured data, a similarity measure for attributed graphs is necessary. All known similarity measures for attributed graphs are either limited to a special type of graph or computationally extremely complex, i.e. NP-complete, and are, therefore, unsuitable for data mining in large databases. In this paper, we present a new similarity measure for attributed graphs, called matching distance. We demonstrate, how the matching distance can be used for efficient similarity search in attributed graphs. Furthermore, we propose a filter-refinement architecture and an accompanying set of filter methods to reduce the number of necessary distance calculations during similarity search. Our experiments show that the matching distance is a meaningful similarity measure for attributed graphs and that it enables efficient clustering of structured data.


Cost Function Similarity Measure Image Retrieval Query Processing Similarity Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hans-Peter Kriegel
    • 1
  • Stefan Schönauer
    • 1
  1. 1.Institute for Computer ScienceUniversity of Munich 

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