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Robust K-means Clustering/Evidence
Method evidence record

Robust K-means Clustering

Robust K-means clustering is an extension of classical k-means that protects cluster estimates from distortion caused by outliers or contaminated observations. By trimming a user-specified fraction of the most extreme points before updating cluster centers, the algorithm yields stable, meaningful partitions even when the data contain atypical cases that would severely bias standard k-means.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Robust K-means Clustering
Taxonomic method record · latent-structure / statistics
  • Cuesta-Albertos, J. A., Gordaliza, A., & Matrán, C. (1997). Trimmed k-means: An attempt to robustify quantizers. The Annals of Statistics, 25(2), 553–576. · DOI 10.1214/aos/1031833664
  • García-Escudero, L. A., Gordaliza, A., Matrán, C., & Mayo-Iscar, A. (2008). A general trimming approach to robust cluster analysis. The Annals of Statistics, 36(3), 1324–1345. · DOI 10.1214/07-AOS515
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Taxonomic bucketCluster Analysismachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMixture Modelingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRobust Hierarchical Clusteringmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRobust Mixture Modelingmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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