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Memorias de investigación
Artículos en revistas:
Semi-supervised projected model-based clustering
Año:2014
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
We present an adaptation of model-based clustering for partially labeled data, that is capable of finding hidden cluster labels. All the originally known and discoverable clusters are represented using localized feature subset selections (subspaces), obtaining clusters unable to be discovered by global feature subset selection. The semi-supervised projected model-based clustering algorithm (SeSProC) also includes a novel model selection approach, using a greedy forward search to estimate the final number of clusters. The quality of SeSProC is assessed using synthetic data, demonstrating its effectiveness, under different data conditions, not only at classifying instances with known labels, but also at discovering completely hidden clusters in different subspaces. Besides, SeSProC also outperforms three related baseline algorithms in most scenarios using synthetic and real data sets.
Internacional
Si
JCR del ISI
Si
Título de la revista
Data Mining And Knowledge Discovery
ISSN
1384-5810
Factor de impacto JCR
2,877
Información de impacto
Datos JCR del año 2012
Volumen
28
DOI
Número de revista
4
Desde la página
882
Hasta la página
917
Mes
SIN MES
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: L. Guerra
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: V. Robles
  • Autor: P. Larrañaga
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Inteligencia Artificial
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