This example uses a large dataset of faces to learn a set of 20 x 20 images patches that constitute faces. From the programming standpoint

This example compares 2 dimensionality reduction strategies: univariate feature selection with Anova feature

Silhouette analysis can be used to study the separation distance between the resulting clusters. The silhouette

The plots display firstly what a K-means algorithm would yield using three clusters. It is then shown what the effect of a bad initialization is on the classification process:

An illustration of various linkage option for agglomerative clustering on a 2D embedding of the digits dataset. The goal

Finds core samples of high density and expands clusters from them. print(__doc__) import numpy as np from

Reference: Dorin Comaniciu and Peter Meer, ?Mean Shift: A robust approach toward feature space analysis?. IEEE Transactions on Pattern Analysis

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

In this example we compare the various initialization strategies for K-means in terms of runtime and quality of the results.

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