.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/cluster/plot_linkage_comparison.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_cluster_plot_linkage_comparison.py: ================================================================ Comparing different hierarchical linkage methods on toy datasets ================================================================ This example shows characteristics of different linkage methods for hierarchical clustering on datasets that are "interesting" but still in 2D. The main observations to make are: - single linkage is fast, and can perform well on non-globular data, but it performs poorly in the presence of noise. - average and complete linkage perform well on cleanly separated globular clusters, but have mixed results otherwise. - Ward is the most effective method for noisy data. While these examples give some intuition about the algorithms, this intuition might not apply to very high dimensional data. .. GENERATED FROM PYTHON SOURCE LINES 24-38 .. code-block:: default print(__doc__) import time import warnings import numpy as np import matplotlib.pyplot as plt from sklearn import cluster, datasets from sklearn.preprocessing import StandardScaler from itertools import cycle, islice np.random.seed(0) .. GENERATED FROM PYTHON SOURCE LINES 39-41 Generate datasets. We choose the size big enough to see the scalability of the algorithms, but not too big to avoid too long running times .. GENERATED FROM PYTHON SOURCE LINES 41-61 .. code-block:: default n_samples = 1500 noisy_circles = datasets.make_circles(n_samples=n_samples, factor=.5, noise=.05) noisy_moons = datasets.make_moons(n_samples=n_samples, noise=.05) blobs = datasets.make_blobs(n_samples=n_samples, random_state=8) no_structure = np.random.rand(n_samples, 2), None # Anisotropicly distributed data random_state = 170 X, y = datasets.make_blobs(n_samples=n_samples, random_state=random_state) transformation = [[0.6, -0.6], [-0.4, 0.8]] X_aniso = np.dot(X, transformation) aniso = (X_aniso, y) # blobs with varied variances varied = datasets.make_blobs(n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=random_state) .. GENERATED FROM PYTHON SOURCE LINES 62-63 Run the clustering and plot .. GENERATED FROM PYTHON SOURCE LINES 63-150 .. code-block:: default # Set up cluster parameters plt.figure(figsize=(9 * 1.3 + 2, 14.5)) plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05, hspace=.01) plot_num = 1 default_base = {'n_neighbors': 10, 'n_clusters': 3} datasets = [ (noisy_circles, {'n_clusters': 2}), (noisy_moons, {'n_clusters': 2}), (varied, {'n_neighbors': 2}), (aniso, {'n_neighbors': 2}), (blobs, {}), (no_structure, {})] for i_dataset, (dataset, algo_params) in enumerate(datasets): # update parameters with dataset-specific values params = default_base.copy() params.update(algo_params) X, y = dataset # normalize dataset for easier parameter selection X = StandardScaler().fit_transform(X) # ============ # Create cluster objects # ============ ward = cluster.AgglomerativeClustering( n_clusters=params['n_clusters'], linkage='ward') complete = cluster.AgglomerativeClustering( n_clusters=params['n_clusters'], linkage='complete') average = cluster.AgglomerativeClustering( n_clusters=params['n_clusters'], linkage='average') single = cluster.AgglomerativeClustering( n_clusters=params['n_clusters'], linkage='single') clustering_algorithms = ( ('Single Linkage', single), ('Average Linkage', average), ('Complete Linkage', complete), ('Ward Linkage', ward), ) for name, algorithm in clustering_algorithms: t0 = time.time() # catch warnings related to kneighbors_graph with warnings.catch_warnings(): warnings.filterwarnings( "ignore", message="the number of connected components of the " + "connectivity matrix is [0-9]{1,2}" + " > 1. Completing it to avoid stopping the tree early.", category=UserWarning) algorithm.fit(X) t1 = time.time() if hasattr(algorithm, 'labels_'): y_pred = algorithm.labels_.astype(int) else: y_pred = algorithm.predict(X) plt.subplot(len(datasets), len(clustering_algorithms), plot_num) if i_dataset == 0: plt.title(name, size=18) colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a', '#f781bf', '#a65628', '#984ea3', '#999999', '#e41a1c', '#dede00']), int(max(y_pred) + 1)))) plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[y_pred]) plt.xlim(-2.5, 2.5) plt.ylim(-2.5, 2.5) plt.xticks(()) plt.yticks(()) plt.text(.99, .01, ('%.2fs' % (t1 - t0)).lstrip('0'), transform=plt.gca().transAxes, size=15, horizontalalignment='right') plot_num += 1 plt.show() .. image:: /auto_examples/cluster/images/sphx_glr_plot_linkage_comparison_001.png :alt: Single Linkage, Average Linkage, Complete Linkage, Ward Linkage :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 2.563 seconds) .. _sphx_glr_download_auto_examples_cluster_plot_linkage_comparison.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_linkage_comparison.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_linkage_comparison.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_linkage_comparison.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_