File Content: plot_kmeans_assumptions.py """ ==================================== Demonstration of k-means assumptions ==================================== This example is meant to illustrate situations where k-means will produce unintuitive and possibly unexpected clusters. In the first three plots, the input data does not conform to some implicit assumption that k-means makes and undesirable clusters are produced as a result. In the last plot, k-means returns intuitive clusters despite unevenly sized blobs. """ print(__doc__) # Author: Phil Roth <mr.phil.roth@gmail.com> # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import KMeans from sklearn.datasets import make_blobs plt.figure(figsize=(12, 12)) n_samples = 1500 random_state = 170 X, y = make_blobs(n_samples=n_samples, random_state=random_state) # Incorrect number of clusters y_pred = KMeans(n_clusters=2, random_state=random_state).fit_predict(X) plt.subplot(221) plt.scatter(X[:, 0], X[:, 1], c=y_pred) plt.title("Incorrect Number of Blobs") # Anisotropicly distributed data transformation = [[ 0.60834549, -0.63667341], [-0.40887718, 0.85253229]] X_aniso = np.dot(X, transformation) y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_aniso) plt.subplot(222) plt.scatter(X_aniso[:, 0], X_aniso[:, 1], c=y_pred) plt.title("Anisotropicly Distributed Blobs") # Different variance X_varied, y_varied = make_blobs(n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=random_state) y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_varied) plt.subplot(223) plt.scatter(X_varied[:, 0], X_varied[:, 1], c=y_pred) plt.title("Unequal Variance") # Unevenly sized blobs X_filtered = np.vstack((X[y == 0][:500], X[y == 1][:100], X[y == 2][:10])) y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_filtered) plt.subplot(224) plt.scatter(X_filtered[:, 0], X_filtered[:, 1], c=y_pred) plt.title("Unevenly Sized Blobs") plt.show() Submit