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

The following plots demonstrate the impact of the number of clusters and number of samples on various clustering performance evaluation

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

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

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

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

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

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

Performs a pixel-wise Vector Quantization (VQ) of an image of the summer palace (China), reducing the number of colors required to show the image from 96,615

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