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 nearest neighbors. Two consequences of imposing a connectivity can be seen. First clustering with a connectivity matrix is much faster. Second, when using a connectivity matrix, average and complete linkage are unstable and tend to create a few clusters that grow very quickly. Indeed, average and complete linkage fight this percolation behavior by considering all the distances between two clusters when merging them. The connectivity graph breaks this mechanism. This effect is more pronounced for very sparse graphs (try decreasing the number of neighbors in kneighbors_graph) and with complete linkage. In particular, having a very small number of neighbors in the graph, imposes a geometry that is close to that of single linkage, which is well known to have this percolation instability.

Filename	Size	Update
plot_face_segmentation.py	2561	2018-01-12
plot_lof.py	2013	2018-01-12
plot_mean_shift.py	1793	2018-01-12
plot_agglomerative_clustering.py	2931	2018-01-12
plot_birch_vs_minibatchkmeans.py	3689	2018-01-12
plot_classifier_comparison.py	5219	2018-01-12
plot_digits_agglomeration.py	1694	2018-01-11
plot_digits_linkage.py	2959	2018-01-12

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