Machine learningMachine learning
正则化 K-均值聚类
正则化 K-均值聚类通过向目标函数添加惩罚项（最常见的是 L1（套索型）或 L2 约束）来扩展标准的 K-均值聚类。这可以防止退化的聚类解，并且在 Witten 和 Tibshirani (2010) 提出的稀疏变体中，可以同时选择驱动聚类分离的特征，因此在高维数据中，当许多特征不相关时，它尤其有价值。
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来源
- Witten, D. M., & Tibshirani, R. (2010). A framework for feature selection in clustering. Journal of the American Statistical Association, 105(490), 713–726. DOI: 10.1198/jasa.2010.tm09415 ↗
- K-means clustering. Wikipedia. link ↗
如何引用本页
ScholarGate. (2026, June 3). Regularized K-Means Clustering. ScholarGate. https://scholargate.app/zh/machine-learning/regularized-k-means
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