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Memorias de investigación
Ponencias en congresos:
C-DenStream: Using Domain Knowledge on a Data Stream
Año:2009
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
Stream clustering algorithms are traditionally designed to process streams efficiently and to adapt to the evolution of the underlying population. This is done without assuming any prior knowledge about the data. However, in many cases, a certain amount of domain or background knowledge is available, and instead of simply using it for the external validation of the clustering results, this knowledge can be used to guide the clustering process. In non-stream data, domain knowledge is exploited in the context of semi-supervised clustering. In this paper, we extend the static semi-supervised learning paradigm for streams. We present C-DenStream, a density-based clustering algorithm for data streams that includes domain information in the form of constraints. We also propose a novel method for the use of background knowledge in data streams. The performance study over a number of real and synthetic data sets demonstrates the effectiveness and efficiency of our method. To our knowledge, this is the first approach to include domain knowledge in clustering for data streams.
Internacional
No
Nombre congreso
Discovery Science 09
Tipo de participación
960
Lugar del congreso
Oporto
Revisores
Si
ISBN o ISSN
0302-9743
DOI
10.1007/978-3-642-04747-3
Fecha inicio congreso
03/10/2009
Fecha fin congreso
05/10/2009
Desde la página
287
Hasta la página
301
Título de las actas
Lecture Notes in Computer Science
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Ernestina Menasalvas Ruiz (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Data Mining Engineering (DaME) Ingeniería de Minería de datos
  • Departamento: Lenguajes y Sistemas Informáticos e Ingeniería de Software
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