<i>k</i>-Center Clustering with Outliers in Sliding Windows

Metric <i>k</i>-center clustering is a fundamental unsupervised learning primitive. Although widely used, this primitive is heavily affected by noise in the data, so a more sensible variant seeks for the best solution that disregards a given number <i>z</i> of points of the dataset, which are called...

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Auteurs principaux:	Paolo Pellizzoni, Andrea Pietracaprina, Geppino Pucci
Format:	Artigo
Langue:	Inglês
Publié:	MDPI AG 2022-01-01
Collection:	Algorithms
Sujets:	<i>k</i>-center with outliers effective diameter big data data stream model sliding windows coreset
Accès en ligne:	https://www.mdpi.com/1999-4893/15/2/52
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