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

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

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:

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

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

This example uses

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