高斯混合模型（Gaussian mixture model）

简单介绍

算法大鉴赏：高斯混合模型（Gaussian mixture model） - 知乎 (zhihu.com)

与k均值区别

最佳聚类实践：高斯混合模型（GMM） - 知乎 (zhihu.com)

GMM不需要对数据做标准化处理

machine learning - Is it important to make a feature scaling before using Gaussian Mixture Model? - Cross Validated (stackexchange.com)

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)

normalization - Should we standardize the data while doing Gaussian process regression? - Cross Validated (stackexchange.com)

算法包

sklearn.mixture.GaussianMixture — scikit-learn 1.1.2 documentation
2.1. Gaussian mixture models — scikit-learn 1.1.2 documentation