机器学习（聚类十一）——不同聚类算法在不同数据分布情况下的聚类效果人工智能张连海-

至此聚类相关的内容告一段落，前面十篇博客介绍了常见的几种聚类算法，也加入了一些代码实现。这篇博客来一个汇总的实例，分别创建圆形数据、月牙形数据、聚团数据以及随机数据，并测试不同数据在各种不同聚类算法中的聚类效果以及消耗时间。

import time import warnings import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt  from sklearn import cluster from sklearn import datasets as ds from sklearn.neighbors import kneighbors_graph from sklearn.preprocessing import StandardScaler  ## 设置属性防止中文乱码 mpl.rcParams['font.sans-serif'] = [u'SimHei'] mpl.rcParams['axes.unicode_minus'] = False warnings.filterwarnings(action='ignore', category=UserWarning) ## 产生模拟数据 n_samples = 1500 np.random.seed(0) #产生圆形的 noisy_circles = ds.make_circles(n_samples=n_samples, factor=.5, noise=.05) #产生月牙形 noisy_moons = ds.make_moons(n_samples=n_samples, noise=.05) #高斯分布 blobs = ds.make_blobs(n_samples=n_samples, n_features=2, cluster_std=0.5, centers=3, random_state=0) no_structure = np.random.rand(n_samples, 2), None  datasets = [noisy_circles, noisy_moons, blobs, no_structure] clusters = [2,2,3,2]  clustering_names = [ 'KMeans', 'MiniBatchKMeans', 'AC-ward', 'AC-average', 'Birch', 'DBSCAN','SpectralClustering'] ## 开始画图 plt.figure(figsize=(len(clustering_names) * 2 + 3, 9.5), facecolor='w') plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05,hspace=.01) colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk']) colors = np.hstack([colors] * 20) plot_num = 1 for i_dataset,(dataset, n_cluster) in enumerate(zip(datasets, clusters)):     X,y = dataset     X = StandardScaler().fit_transform(X)     connectivity = kneighbors_graph(X, n_neighbors=10, include_self=False)     connectivity = 0.5 * (connectivity + connectivity.T)          km = cluster.KMeans(n_clusters=n_cluster)     mbkm = cluster.MiniBatchKMeans(n_clusters=n_cluster)     ward = cluster.AgglomerativeClustering(n_clusters=n_cluster,connectivity=connectivity, linkage='ward')     average = cluster.AgglomerativeClustering(n_clusters=n_cluster,connectivity=connectivity, linkage='average')     birch = cluster.Birch(n_clusters=n_cluster)     dbscan = cluster.DBSCAN(eps=.2)     spectral = cluster.SpectralClustering(n_clusters=n_cluster, eigen_solver='arpack', affinity="nearest_neighbors")     clustering_algorithms = [km, mbkm, ward, average, birch, dbscan, spectral] for name, algorithm in zip(clustering_names, clustering_algorithms):         t0 = time.time()         algorithm.fit(X)         t1 = time.time() # 如果模型中存在"labels__"这个属性的话，那么获取这个预测的类别值 if hasattr(algorithm, 'labels_'):             y_pred = algorithm.labels_.astype(np.int) else:             y_pred = algorithm.predict(X) # 画子图         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) # 如果模型有中心点属性，那么画出中心点 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() 

最后看一下运行效果