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Unsupervised ensemble minority clustering

Autor
González, E.; Turmo, J.
Tipus d'activitat
Document cientificotècnic
Data
2012-03
Codi
LSI-12-4-R
Repositori
http://hdl.handle.net/2117/15664 Obrir en finestra nova
URL
http://www.lsi.upc.edu/dept/techreps/llistat_detallat.php?id=1117 Obrir en finestra nova
Resum
Cluster a alysis lies at the core of most unsupervised learning tasks. However, the majority of clustering algorithms depend on the all-in assumption, in which all objects belong to some cluster, and perform poorly on minority clustering tasks, in which a small fraction of signal data stands against a majority of noise. The approaches proposed so far for minority clustering are supervised: they require the number and distribution of the foreground and background clusters. In supervised learning ...
Citació
González, E.; Turmo, J. "Unsupervised ensemble minority clustering". 2012.
Grup de recerca
GPLN - Grup de Processament del Llenguatge Natural
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
TALP - Centre de Tecnologies i Aplicacions del Llenguatge i la Parla

Participants

Arxius