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

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

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

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

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:

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 uses

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

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