sklearn.cluster.KMeans — scikit-learn 1.0.2 documentation
https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.htmlsklearn.cluster.KMeans¶ class sklearn.cluster. KMeans (n_clusters = 8, *, init = 'k-means++', n_init = 10, max_iter = 300, tol = 0.0001, verbose = 0, random_state = None, copy_x = True, algorithm = 'auto') [source] ¶. K-Means clustering. Read more in the User Guide.. Parameters n_clusters int, default=8. The number of clusters to form as well as the number of centroids to generate.
kmeans-smote · PyPI
pypi.org › project › kmeans-smoteMar 30, 2019 · K-Means SMOTE is an oversampling method for class-imbalanced data. It aids classification by generating minority class samples in safe and crucial areas of the input space. The method avoids the generation of noise and effectively overcomes imbalances between and within classes. This project is a python implementation of k-means SMOTE.