Abstract
Graphs are a powerful and versatile tool useful for representing patterns in various subfields of science and engineering. In many applications, for example, in pattern recognition and computer vision, it is required to measure the similarity of objects for clustering similar patterns. In this paper a new structural method, the Weighted Minimum Common Supergraph (WMCS), for representing a cluster of patterns is proposed. Using this method it becomes easy to extract the common information shared in the patterns of a cluster and separate this information from noise and distortions that usually affect graphs representing real objects. Moreover, experimental results show that WMCS is suitable for performing graph clustering.
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Bunke, H., Foggia, P., Guidobaldi, C., Vento, M. (2003). Graph Clustering Using the Weighted Minimum Common Supergraph. In: Hancock, E., Vento, M. (eds) Graph Based Representations in Pattern Recognition. GbRPR 2003. Lecture Notes in Computer Science, vol 2726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45028-9_21
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DOI: https://doi.org/10.1007/3-540-45028-9_21
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