This example is meant to illustrate situations where k-means will produce unintuitive and possibly unexpected clusters. In the first three plots, the input

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

This example uses

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

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.

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

Evaluate the ability of k-means initializations strategies to make the algorithm convergence robust as measured by the relative

Demonstrates the effect of different metrics on the hierarchical clustering. The example is engineered to show the effect of the choice