This example uses

This example shows the effect of imposing a connectivity graph to capture local structure in the data. The graph is simply the graph of 20

These images how similar features are merged together using feature agglomeration.

We want to compare the performance of the MiniBatchKMeans and KMeans: the MiniBatchKMeans is faster, but gives slightly different

Face, a 1024 x 768 size image of a raccoon face, is used here to illustrate how k-means is used for vector quantization.

Compute the segmentation of a 2D image with Ward hierarchical clustering. The clustering is spatially constrained in

In this example, an image with connected circles is generated and spectral clustering is used to separate the circles. In these settings, the

Example builds a swiss roll dataset and runs hierarchical clustering on their position. For more information, see

This example compares the timing of Birch (with and without the global clustering step) and MiniBatchKMeans on a synthetic dataset having 100,000 samples and

Reference: Brendan J. Frey and Delbert Dueck, ?Clustering by Passing Messages Between Data Points?, Science Feb. 2007