簡單介紹
算法大鑒賞：高斯混合模型（Gaussian mixture model） - 知乎 (zhihu.com)
與k均值區(qū)別
最佳聚類實踐：高斯混合模型（GMM） - 知乎 (zhihu.com)
GMM不需要對數(shù)據(jù)做標準化處理
Gaussian Mixture Modelling explicitly relaxes both the assumption of all clusters having the same variance, and the assumption of no correlation of features within a cluster, and that's why you don't need to standardise your features.
To be clear, the real advantage to using Gaussian Mixture Models is that your clusters don't have to be hyper-spherical and of the same radius. The fact that you also don't have to standardise your variables is just a nice bonus
Gaussian process regression (GPR)
算法包
sklearn.mixture.GaussianMixture — scikit-learn 1.1.2 documentation
2.1. Gaussian mixture models — scikit-learn 1.1.2 documentation
