.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/cluster/plot_digits_linkage.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_cluster_plot_digits_linkage.py: ============================================================================= Various Agglomerative Clustering on a 2D embedding of digits ============================================================================= An illustration of various linkage option for agglomerative clustering on a 2D embedding of the digits dataset. The goal of this example is to show intuitively how the metrics behave, and not to find good clusters for the digits. This is why the example works on a 2D embedding. What this example shows us is the behavior "rich getting richer" of agglomerative clustering that tends to create uneven cluster sizes. This behavior is pronounced for the average linkage strategy, that ends up with a couple of clusters with few datapoints. The case of single linkage is even more pathologic with a very large cluster covering most digits, an intermediate size (clean) cluster with most zero digits and all other clusters being drawn from noise points around the fringes. The other linkage strategies lead to more evenly distributed clusters that are therefore likely to be less sensible to a random resampling of the dataset. .. GENERATED FROM PYTHON SOURCE LINES 29-89 .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_digits_linkage_001.png :alt: ward linkage :srcset: /auto_examples/cluster/images/sphx_glr_plot_digits_linkage_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_digits_linkage_002.png :alt: average linkage :srcset: /auto_examples/cluster/images/sphx_glr_plot_digits_linkage_002.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_digits_linkage_003.png :alt: complete linkage :srcset: /auto_examples/cluster/images/sphx_glr_plot_digits_linkage_003.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_digits_linkage_004.png :alt: single linkage :srcset: /auto_examples/cluster/images/sphx_glr_plot_digits_linkage_004.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out .. code-block:: none Computing embedding Done. ward : 0.05s average : 0.05s complete : 0.05s single : 0.02s | .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause from time import time import numpy as np from matplotlib import pyplot as plt from sklearn import datasets, manifold digits = datasets.load_digits() X, y = digits.data, digits.target n_samples, n_features = X.shape np.random.seed(0) # ---------------------------------------------------------------------- # Visualize the clustering def plot_clustering(X_red, labels, title=None): x_min, x_max = np.min(X_red, axis=0), np.max(X_red, axis=0) X_red = (X_red - x_min) / (x_max - x_min) plt.figure(figsize=(6, 4)) for digit in digits.target_names: plt.scatter( *X_red[y == digit].T, marker=f"${digit}$", s=50, c=plt.cm.nipy_spectral(labels[y == digit] / 10), alpha=0.5, ) plt.xticks([]) plt.yticks([]) if title is not None: plt.title(title, size=17) plt.axis("off") plt.tight_layout(rect=[0, 0.03, 1, 0.95]) # ---------------------------------------------------------------------- # 2D embedding of the digits dataset print("Computing embedding") X_red = manifold.SpectralEmbedding(n_components=2).fit_transform(X) print("Done.") from sklearn.cluster import AgglomerativeClustering for linkage in ("ward", "average", "complete", "single"): clustering = AgglomerativeClustering(linkage=linkage, n_clusters=10) t0 = time() clustering.fit(X_red) print("%s :\t%.2fs" % (linkage, time() - t0)) plot_clustering(X_red, clustering.labels_, "%s linkage" % linkage) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.436 seconds) .. _sphx_glr_download_auto_examples_cluster_plot_digits_linkage.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.6.X?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_digits_linkage.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/cluster/plot_digits_linkage.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_digits_linkage.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_digits_linkage.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_digits_linkage.zip ` .. include:: plot_digits_linkage.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_