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A Modification of the k-Means Method for Quasi-Unsupervised Learning

Autor
Rebollo-Monedero, D.; Solé, M.; Nin, J.; Forne, J.
Tipus d'activitat
Article en revista
Revista
Knowledge-based systems
Data de publicació
2013-01
Volum
37
Pàgina inicial
176
Pàgina final
185
DOI
https://doi.org/10.1016/j.knosys.2012.07.024 Obrir en finestra nova
URL
http://www.sciencedirect.com/science/article/pii/S0950705112002122?v=s5 Obrir en finestra nova
Resum
Since the advent of data clustering, the original formulation of the clustering problem has been enriched to incorporate a number of twists to widen its range of application. In particular, recent heuristic approaches have proposed to incorporate restrictions on the size of the clusters, while striving to minimize a measure of dissimilarity within them. Such size constraints effectively constitute a way to exploit prior knowledge, readily available in many scenarios, which can lead to an improve...
Paraules clau
Clustering, Constrained clustering, Loyd algorithm, Quasi-unsupervised learning, Size constraints, k-Means method
Grup de recerca
CAP - Grup de Computació d'Altes Prestacions
SISCOM - Smart Services for Information Systems and Communication Networks

Participants