Machine learningMachine learning
鲁棒k均值
鲁棒k均值是经典k均值聚类的变体，旨在抵抗离群值的影响。通过在计算聚类中心之前修剪掉指定比例的最极端观测值，即使数据包含噪声、污染或重尾分布（标准k均值在此类情况下会失效），它也能产生稳定且有意义的划分。
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来源
- 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 ↗
- 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 ↗
如何引用本页
ScholarGate. (2026, June 3). Robust k-means Clustering. ScholarGate. https://scholargate.app/zh/machine-learning/robust-k-means
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