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dc.contributor.authorFerrer, Miguel
dc.contributor.authorSerratosa, Francesco
dc.contributor.authorRiesen, Kaspar
dc.contributor.editorPerner, Petra
dc.date.accessioned2015-10-08T10:17:57Z
dc.date.available2015-10-08T10:17:57Z
dc.date.issued2015
dc.identifier.isbn978-3-319-21023-0
dc.identifier.urihttp://hdl.handle.net/11654/10151
dc.description.abstractIn data mining systems, which operate on complex data with structural relationships, graphs are often used to represent the basic objects under study. Yet, the high representational power of graphs is also accompanied by an increased complexity of the associated algorithms. Exact graph similarity or distance, for instance, can be computed in exponential time only. Recently, an algorithmic framework that allows graph dissimilarity computation in cubic time with respect to the number of nodes has been presented. This fast computation is at the expense, however, of generally overestimating the true distance. The present paper introduces six different post-processing algorithms that can be integrated in this suboptimal graph distance framework. These novel extensions aim at improving the overall distance quality while keeping the low computation time of the approximation. An experimental evaluation clearly shows that the proposed heuristics substantially reduce the overestimation in the existing approximation framework while the computation time remains remarkably low.
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofMachine Learning and Data Mining in Pattern Recognition - 11th International Conference, MLDM 2015, Hamburg, Germany, July 20-21, 2015
dc.relation.ispartofseriesLecture Notes in Computer Science;9166
dc.accessRightsAnonymous
dc.titleLearning Heuristics to Reduce the Overestimation of Bipartite Graph Edit Distance Approximation
dc.type04 - Beitrag Sammelband oder Konferenzschrift
dc.spatialHamburg
dc.event.start2015-07-20
dc.event.end2015-07-21
dc.audienceScience
fhnw.publicationStatePublished
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publication
fhnw.InventedHereYes
fhnw.PublishedSwitzerlandNo
fhnw.pagination17-31
fhnw.IsStudentsWorkno


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