方法证据记录
Regularized k-means
Regularized k-means extends standard k-means by adding a penalty term — most commonly an L1 (lasso-type) or L2 constraint — to the objective function. This discourages degenerate cluster solutions and, in the sparse variant introduced by Witten and Tibshirani (2010), simultaneously selects the features that drive cluster separation, making it especially valuable in high-dimensional settings where many features are irrelevant.
源记录
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Regularized K-Means Clustering
分类方法记录 · ml-model / machine-learning
- 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. · URL
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