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dc.contributor.authorBifet, Albert
dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorGavaldà, Ricard
dc.coverage.spatialConference held at San Diego, CAen_NZ
dc.identifier.citationBifet, A., Holmes, G., Pfahringer, B., & Gavaldà, R. (2011). Mining frequent closed graphs on evolving data streams. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 21-24, 2011, San Diego, California, USA (pp. 591-599). New York, USA: ACM.en_NZ
dc.description.abstractGraph mining is a challenging task by itself, and even more so when processing data streams which evolve in real-time. Data stream mining faces hard constraints regarding time and space for processing, and also needs to provide for concept drift detection. In this paper we present a framework for studying graph pattern mining on time-varying streams. Three new methods for mining frequent closed subgraphs are presented. All methods work on coresets of closed subgraphs, compressed representations of graph sets, and maintain these sets in a batch-incremental manner, but use different approaches to address potential concept drift. An evaluation study on datasets comprising up to four million graphs explores the strength and limitations of the proposed methods. To the best of our knowledge this is the first work on mining frequent closed subgraphs in non-stationary data streams.
dc.relation.ispartofProceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11
dc.subjectcomputer scienceen_NZ
dc.subjectdata streamsen_NZ
dc.subjectclosed miningen_NZ
dc.subjectconcept driften_NZ
dc.subjectMachine learning
dc.titleMining frequent closed graphs on evolving data streamsen_NZ
dc.typeConference Contributionen_NZ
dc.relation.isPartOfProc 17th ACM SIGKDD Conference on Knowledge Discovery and Data Miningen_NZ
pubs.finish-date2011-08-24en_NZ York, NYen_NZ

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