File Content: plot_cluster_comparison.py """ ========================================================= Comparing different clustering algorithms on toy datasets ========================================================= This example aims at showing characteristics of different clustering algorithms on datasets that are "interesting" but still in 2D. The last dataset is an example of a 'null' situation for clustering: the data is homogeneous, and there is no good clustering. While these examples give some intuition about the algorithms, this intuition might not apply to very high dimensional data. The results could be improved by tweaking the parameters for each clustering strategy, for instance setting the number of clusters for the methods that needs this parameter specified. Note that affinity propagation has a tendency to create many clusters. Thus in this example its two parameters (damping and per-point preference) were set to mitigate this behavior. """ print(__doc__) import time import numpy as np import matplotlib.pyplot as plt from sklearn import cluster, datasets from sklearn.neighbors import kneighbors_graph from sklearn.preprocessing import StandardScaler np.random.seed(0) # 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 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 colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk']) colors = np.hstack([colors] * 20) clustering_names = [ 'MiniBatchKMeans', 'AffinityPropagation', 'MeanShift', 'SpectralClustering', 'Ward', 'AgglomerativeClustering', 'DBSCAN', 'Birch'] plt.figure(figsize=(len(clustering_names) * 2 + 3, 9.5)) plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05, hspace=.01) plot_num = 1 datasets = [noisy_circles, noisy_moons, blobs, no_structure] for i_dataset, dataset in enumerate(datasets): X, y = dataset # normalize dataset for easier parameter selection X = StandardScaler().fit_transform(X) # estimate bandwidth for mean shift bandwidth = cluster.estimate_bandwidth(X, quantile=0.3) # connectivity matrix for structured Ward connectivity = kneighbors_graph(X, n_neighbors=10, include_self=False) # make connectivity symmetric connectivity = 0.5 * (connectivity + connectivity.T) # create clustering estimators ms = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True) two_means = cluster.MiniBatchKMeans(n_clusters=2) ward = cluster.AgglomerativeClustering(n_clusters=2, linkage='ward', connectivity=connectivity) spectral = cluster.SpectralClustering(n_clusters=2, eigen_solver='arpack', affinity="nearest_neighbors") dbscan = cluster.DBSCAN(eps=.2) affinity_propagation = cluster.AffinityPropagation(damping=.9, preference=-200) average_linkage = cluster.AgglomerativeClustering( linkage="average", affinity="cityblock", n_clusters=2, connectivity=connectivity) birch = cluster.Birch(n_clusters=2) clustering_algorithms = [ two_means, affinity_propagation, ms, spectral, ward, average_linkage, dbscan, birch] for name, algorithm in zip(clustering_names, clustering_algorithms): # predict cluster memberships t0 = time.time() algorithm.fit(X) t1 = time.time() if hasattr(algorithm, 'labels_'): y_pred = algorithm.labels_.astype(np.int) else: y_pred = algorithm.predict(X) # plot plt.subplot(4, len(clustering_algorithms), plot_num) if i_dataset == 0: plt.title(name, size=18) plt.scatter(X[:, 0], X[:, 1], color=colors[y_pred].tolist(), s=10) if hasattr(algorithm, 'cluster_centers_'): centers = algorithm.cluster_centers_ center_colors = colors[:len(centers)] plt.scatter(centers[:, 0], centers[:, 1], s=100, c=center_colors) plt.xlim(-2, 2) plt.ylim(-2, 2) 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() Submit