ScholarGate
Assistent
Machine learningMachine learning

Robust k-means

Robust k-means er en variant af klassisk k-means-klyngedannelse, der er designet til at modstå indflydelsen fra outliers. Ved at trimme en specificeret fraktion af de mest ekstreme observationer, før klyngecentre beregnes, producerer den stabile og meningsfulde opdelinger, selv når data indeholder støj, kontaminering eller tung-halede fordelinger – situationer hvor standard k-means bryder sammen.

Åbn i MethodMindSnartVideoSnartDownload slides

Læs hele metoden

Kun for medlemmer

Log ind med en gratis konto for at læse dette afsnit.

Log ind

Method map

The neighbourhood of related methods — select a node to explore.

Kilder

  1. Garcia-Escudero, L. A., & Gordaliza, A. (1999). Robustness properties of k-means and trimmed k-means. Journal of the American Statistical Association, 94(447), 956–969. DOI: 10.2307/2670010
  2. Garcia-Escudero, L. A., Gordaliza, A., Matrán, C., & Mayo-Iscar, A. (2008). A general trimming approach to robust cluster analysis. Annals of Statistics, 36(3), 1324–1345. DOI: 10.1214/07-AOS515

Sådan citerer du denne side

ScholarGate. (2026, June 3). Robust k-means Clustering. ScholarGate. https://scholargate.app/da/machine-learning/robust-k-means

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

Compare side by side

Refereret af

ScholarGateRobust k-means (Robust k-means Clustering). Hentet 2026-06-15 fra https://scholargate.app/da/machine-learning/robust-k-means · Datasæt: https://doi.org/10.5281/zenodo.20539026