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K-means vs Mini Batch K-means: a comparison

Autor
Bejar, J.
Tipus d'activitat
Document cientificotècnic
Data
2013-05-17
Codi
LSI-13-8-R
Projecte finançador
SUstainable and PERsuasive Human Users moBility in future cities
Sistema inteligente IWALKER: rehabilitación colaborativa
Repositori
http://hdl.handle.net/2117/23414 Obrir en finestra nova
URL
http://www.lsi.upc.edu/~techreps/files/R13-8.zip Obrir en finestra nova
Resum
Mini Batch K-means (cite{Sculley2010}) has been proposed as an alternative to the K-means algorithm for clustering massive datasets. The advantage of this algorithm is to reduce the computational cost by not using all the dataset each iteration but a subsample of a fixed size. This strategy reduces the number of distance computations per iteration at the cost of lower cluster quality. The purpose of this paper is to perform empirical experiments using artificial datasets with controlled characte...
Citació
Bejar, J. "K-means vs Mini Batch K-means: a comparison". 2013.
Paraules clau
K-means, Scalable algorithms, Unsupervised learning
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
KEMLG - Grup d´Enginyeria del Coneixement i Aprenentatge Automàtic

Participants

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