针对toy datasets的不同聚类方法比较

Comparing different clustering algorithms on toy datasets

针对toy datasets的不同聚类方法比较

原地址 http://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html

       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, andthere is no good clustering.

       这个例子是为了说明不同聚类算法在2维空间下的特性。这些新数据是聚类分析针对“空”的情形：数据是均匀则没有好的簇。

       While these examples give some intuition about the algorithms,this intuition might not apply to very high dimensional data.

       而这些例子仅仅给出算法的一些直观的例子，这些例子未必适用于高维数据。

       The results could be improved by tweaking the parameters foreach clustering strategy, for instance setting the number ofclusters for the methods that needs this parameterspecified. 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 to mitigate this behavior.

        可以通过修改参数来提高聚类效果。例如通过设置簇的个数来设置。需要注意的是，临近扩展成为一种生成簇的趋势。因此，例子中有两个参数（衰减和点偏）被用来设置。

Python source code: plot_cluster_comparison.py

print(__doc__)import timeimport numpy as npimport matplotlib.pyplot as pltfrom sklearn import cluster, datasetsfrom sklearn.neighbors import kneighbors_graphfrom sklearn.preprocessing import StandardScalernp.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 timesn_samples = 1500noisy_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), Nonecolors = 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 = 1datasets = [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 += 1plt.show()