Kernel K-means.

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Kernel K-means.

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Last edited Nov 14, 2013

Created on Feb 15, 2019

This example demonstrates kernel k-means clustering, which extends standard k-means to nonlinear decision boundaries by computing distances in a kernel-induced feature space. The code, written by Mathieu Blondel, implements a custom `KernelKMeans` estimator using scikit-learn’s `BaseEstimator` and `ClusterMixin`, with distance calculations based on pairwise kernels from `sklearn.metrics.pairwise`. It generates synthetic blob data via `make_blobs` from `sklearn.datasets` and fits the model with a linear kernel by default, printing predicted labels for the first ten samples. The implementation iteratively reassigns clusters while monitoring convergence via a tolerance threshold.

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