Adding a new kmeans example.

Author: Phil Roth

examples/cluster/plot_kmeans_assumptions.py

@@ -0,0 +1,63 @@
+"""
+====================================
+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 <[email protected]>
+# 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()