Título:
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Mining frequent closed graphs on evolving data streams.
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Autor/a:
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Bifet Figuerol, Albert Carles; Holmes, Geoff; Pfahringer, Bernhard; Gavaldà Mestre, Ricard
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge |
Abstract:
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Graph 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. |
Abstract:
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Peer Reviewed |
Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació -Data mining -- Data processing -Mineria de dades -- Mètodes estadístics |
Derechos:
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Tipo de documento:
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Artículo - Versión publicada Objeto de conferencia |
Editor:
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ACM Press, NY
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