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dc.contributor.authorGhazizadeh, Shayanen_US
dc.contributor.authorChawathe, Sudarshanen_US
dc.date.accessioned2004-05-31T23:18:22Z
dc.date.available2004-05-31T23:18:22Z
dc.date.created2002-05en_US
dc.date.issued2002-05-22en_US
dc.identifier.urihttp://hdl.handle.net/1903/1199
dc.description.abstractWe study the problem of finding frequent structures in semistructured data (represented as a directed labeled graph). Frequent structures are graphs that are isomorphic to a large number of subgraphs in the data graph. Frequent structures form building blocks for visual exploration and data mining of semistructured data. We overcome the inherent computational complexity of the problem by using a summary data structure to prune the search space and to provide interactive feedback. We present an experimental study of our methods operating on real datasets. The implementation of our methods (which is freely available) is capable of operating on datasets that are two to three orders of magnitude larger than those described in prior work. (Also UMIACS-TR-2002-44)en_US
dc.format.extent579903 bytes
dc.format.mimetypeapplication/postscript
dc.language.isoen_US
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-4364en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-2002-44en_US
dc.titleDiscovering Frequent Structures using Summariesen_US
dc.typeTechnical Reporten_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US


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