.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/cluster/plot_kmeans_assumptions.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_kmeans_assumptions.py: ==================================== Demonstration of k-means assumptions ==================================== This example is meant to illustrate situations where k-means produces unintuitive and possibly undesirable clusters. .. GENERATED FROM PYTHON SOURCE LINES 10-15 .. code-block:: Python # Author: Phil Roth # Arturo Amor # License: BSD 3 clause .. GENERATED FROM PYTHON SOURCE LINES 16-22 Data generation --------------- The function :func:`~sklearn.datasets.make_blobs` generates isotropic (spherical) gaussian blobs. To obtain anisotropic (elliptical) gaussian blobs one has to define a linear `transformation`. .. GENERATED FROM PYTHON SOURCE LINES 22-41 .. code-block:: Python import numpy as np from sklearn.datasets import make_blobs n_samples = 1500 random_state = 170 transformation = [[0.60834549, -0.63667341], [-0.40887718, 0.85253229]] X, y = make_blobs(n_samples=n_samples, random_state=random_state) X_aniso = np.dot(X, transformation) # Anisotropic blobs X_varied, y_varied = make_blobs( n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=random_state ) # Unequal variance X_filtered = np.vstack( (X[y == 0][:500], X[y == 1][:100], X[y == 2][:10]) ) # Unevenly sized blobs y_filtered = [0] * 500 + [1] * 100 + [2] * 10 .. GENERATED FROM PYTHON SOURCE LINES 42-43 We can visualize the resulting data: .. GENERATED FROM PYTHON SOURCE LINES 43-63 .. code-block:: Python import matplotlib.pyplot as plt fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(12, 12)) axs[0, 0].scatter(X[:, 0], X[:, 1], c=y) axs[0, 0].set_title("Mixture of Gaussian Blobs") axs[0, 1].scatter(X_aniso[:, 0], X_aniso[:, 1], c=y) axs[0, 1].set_title("Anisotropically Distributed Blobs") axs[1, 0].scatter(X_varied[:, 0], X_varied[:, 1], c=y_varied) axs[1, 0].set_title("Unequal Variance") axs[1, 1].scatter(X_filtered[:, 0], X_filtered[:, 1], c=y_filtered) axs[1, 1].set_title("Unevenly Sized Blobs") plt.suptitle("Ground truth clusters").set_y(0.95) plt.show() .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_kmeans_assumptions_001.png :alt: Ground truth clusters, Mixture of Gaussian Blobs, Anisotropically Distributed Blobs, Unequal Variance, Unevenly Sized Blobs :srcset: /auto_examples/cluster/images/sphx_glr_plot_kmeans_assumptions_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 64-85 Fit models and plot results --------------------------- The previously generated data is now used to show how :class:`~sklearn.cluster.KMeans` behaves in the following scenarios: - Non-optimal number of clusters: in a real setting there is no uniquely defined **true** number of clusters. An appropriate number of clusters has to be decided from data-based criteria and knowledge of the intended goal. - Anisotropically distributed blobs: k-means consists of minimizing sample's euclidean distances to the centroid of the cluster they are assigned to. As a consequence, k-means is more appropriate for clusters that are isotropic and normally distributed (i.e. spherical gaussians). - Unequal variance: k-means is equivalent to taking the maximum likelihood estimator for a "mixture" of k gaussian distributions with the same variances but with possibly different means. - Unevenly sized blobs: there is no theoretical result about k-means that states that it requires similar cluster sizes to perform well, yet minimizing euclidean distances does mean that the more sparse and high-dimensional the problem is, the higher is the need to run the algorithm with different centroid seeds to ensure a global minimal inertia. .. GENERATED FROM PYTHON SOURCE LINES 85-114 .. code-block:: Python from sklearn.cluster import KMeans common_params = { "n_init": "auto", "random_state": random_state, } fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(12, 12)) y_pred = KMeans(n_clusters=2, **common_params).fit_predict(X) axs[0, 0].scatter(X[:, 0], X[:, 1], c=y_pred) axs[0, 0].set_title("Non-optimal Number of Clusters") y_pred = KMeans(n_clusters=3, **common_params).fit_predict(X_aniso) axs[0, 1].scatter(X_aniso[:, 0], X_aniso[:, 1], c=y_pred) axs[0, 1].set_title("Anisotropically Distributed Blobs") y_pred = KMeans(n_clusters=3, **common_params).fit_predict(X_varied) axs[1, 0].scatter(X_varied[:, 0], X_varied[:, 1], c=y_pred) axs[1, 0].set_title("Unequal Variance") y_pred = KMeans(n_clusters=3, **common_params).fit_predict(X_filtered) axs[1, 1].scatter(X_filtered[:, 0], X_filtered[:, 1], c=y_pred) axs[1, 1].set_title("Unevenly Sized Blobs") plt.suptitle("Unexpected KMeans clusters").set_y(0.95) plt.show() .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_kmeans_assumptions_002.png :alt: Unexpected KMeans clusters, Non-optimal Number of Clusters, Anisotropically Distributed Blobs, Unequal Variance, Unevenly Sized Blobs :srcset: /auto_examples/cluster/images/sphx_glr_plot_kmeans_assumptions_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 115-121 Possible solutions ------------------ For an example on how to find a correct number of blobs, see :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py`. In this case it suffices to set `n_clusters=3`. .. GENERATED FROM PYTHON SOURCE LINES 121-127 .. code-block:: Python y_pred = KMeans(n_clusters=3, **common_params).fit_predict(X) plt.scatter(X[:, 0], X[:, 1], c=y_pred) plt.title("Optimal Number of Clusters") plt.show() .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_kmeans_assumptions_003.png :alt: Optimal Number of Clusters :srcset: /auto_examples/cluster/images/sphx_glr_plot_kmeans_assumptions_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 128-131 To deal with unevenly sized blobs one can increase the number of random initializations. In this case we set `n_init=10` to avoid finding a sub-optimal local minimum. For more details see :ref:`kmeans_sparse_high_dim`. .. GENERATED FROM PYTHON SOURCE LINES 131-139 .. code-block:: Python y_pred = KMeans(n_clusters=3, n_init=10, random_state=random_state).fit_predict( X_filtered ) plt.scatter(X_filtered[:, 0], X_filtered[:, 1], c=y_pred) plt.title("Unevenly Sized Blobs \nwith several initializations") plt.show() .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_kmeans_assumptions_004.png :alt: Unevenly Sized Blobs with several initializations :srcset: /auto_examples/cluster/images/sphx_glr_plot_kmeans_assumptions_004.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 140-150 As anisotropic and unequal variances are real limitations of the k-means algorithm, here we propose instead the use of :class:`~sklearn.mixture.GaussianMixture`, which also assumes gaussian clusters but does not impose any constraints on their variances. Notice that one still has to find the correct number of blobs (see :ref:`sphx_glr_auto_examples_mixture_plot_gmm_selection.py`). For an example on how other clustering methods deal with anisotropic or unequal variance blobs, see the example :ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py`. .. GENERATED FROM PYTHON SOURCE LINES 150-166 .. code-block:: Python from sklearn.mixture import GaussianMixture fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 6)) y_pred = GaussianMixture(n_components=3).fit_predict(X_aniso) ax1.scatter(X_aniso[:, 0], X_aniso[:, 1], c=y_pred) ax1.set_title("Anisotropically Distributed Blobs") y_pred = GaussianMixture(n_components=3).fit_predict(X_varied) ax2.scatter(X_varied[:, 0], X_varied[:, 1], c=y_pred) ax2.set_title("Unequal Variance") plt.suptitle("Gaussian mixture clusters").set_y(0.95) plt.show() .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_kmeans_assumptions_005.png :alt: Gaussian mixture clusters, Anisotropically Distributed Blobs, Unequal Variance :srcset: /auto_examples/cluster/images/sphx_glr_plot_kmeans_assumptions_005.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 167-180 Final remarks ------------- In high-dimensional spaces, Euclidean distances tend to become inflated (not shown in this example). Running a dimensionality reduction algorithm prior to k-means clustering can alleviate this problem and speed up the computations (see the example :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`). In the case where clusters are known to be isotropic, have similar variance and are not too sparse, the k-means algorithm is quite effective and is one of the fastest clustering algorithms available. This advantage is lost if one has to restart it several times to avoid convergence to a local minimum. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.098 seconds) .. _sphx_glr_download_auto_examples_cluster_plot_kmeans_assumptions.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/main?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_kmeans_assumptions.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/cluster/plot_kmeans_assumptions.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_kmeans_assumptions.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_kmeans_assumptions.py ` .. include:: plot_kmeans_assumptions.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_