能夠?qū)W習(xí)和掌握編程，最好的學(xué)習(xí)方式，就是去掌握基本的使用技巧，再多的概念意義，總歸都是為了使用服務(wù)的，K-means算法又叫K-均值算法，是非監(jiān)督學(xué)習(xí)中的聚類算法。主要有三個(gè)元素，其中N是元素個(gè)數(shù)，x表示元素，c(j)表示第j簇的質(zhì)心，下面就使用方式給大家簡(jiǎn)單介紹實(shí)例使用。

K-Means算法進(jìn)行聚類分析

km = KMeans(n_clusters = 3)km.fit(X)centers = km.cluster_centers_print(centers)

三個(gè)簇的中心點(diǎn)坐標(biāo)為：

[[5.006 3.428 ]

[6.81276596 3.07446809]

[5.77358491 2.69245283]]

比較一下K-Means聚類結(jié)果和實(shí)際樣本之間的差別：

predicted_labels = km.labels_fig, axes = plt.subplots(1, 2, figsize=(16,8))axes[0].scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1, edgecolor=’k’, s=150)axes[1].scatter(X[:, 0], X[:, 1], c=predicted_labels, cmap=plt.cm.Set1,edgecolor=’k’, s=150)axes[0].set_xlabel(’Sepal length’, fontsize=16)axes[0].set_ylabel(’Sepal width’, fontsize=16)axes[1].set_xlabel(’Sepal length’, fontsize=16)axes[1].set_ylabel(’Sepal width’, fontsize=16)axes[0].tick_params(direction=’in’, length=10, width=5, colors=’k’, labelsize=20)axes[1].tick_params(direction=’in’, length=10, width=5, colors=’k’, labelsize=20)axes[0].set_title(’Actual’, fontsize=18)axes[1].set_title(’Predicted’, fontsize=18)

k-means算法實(shí)例擴(kuò)展內(nèi)容：

# -*- coding: utf-8 -*- '''Excercise 9.4'''import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport sysimport randomdata = pd.read_csv(filepath_or_buffer = ’../dataset/watermelon4.0.csv’, sep = ’,’)[['密度','含糖率']].values########################################## K-means ####################################### k = int(sys.argv[1])#Randomly choose k samples from data as mean vectorsmean_vectors = random.sample(data,k)def dist(p1,p2): return np.sqrt(sum((p1-p2)*(p1-p2)))while True: print mean_vectors clusters = map ((lambda x:[x]), mean_vectors) for sample in data: distances = map((lambda m: dist(sample,m)), mean_vectors) min_index = distances.index(min(distances)) clusters[min_index].append(sample) new_mean_vectors = [] for c,v in zip(clusters,mean_vectors): new_mean_vector = sum(c)/len(c) #If the difference betweenthe new mean vector and the old mean vector is less than 0.0001 #then do not updata the mean vector if all(np.divide((new_mean_vector-v),v) < np.array([0.0001,0.0001]) ): new_mean_vectors.append(v) else: new_mean_vectors.append(new_mean_vector) if np.array_equal(mean_vectors,new_mean_vectors): break else: mean_vectors = new_mean_vectors #Show the clustering resulttotal_colors = [’r’,’y’,’g’,’b’,’c’,’m’,’k’]colors = random.sample(total_colors,k)for cluster,color in zip(clusters,colors): density = map(lambda arr:arr[0],cluster) sugar_content = map(lambda arr:arr[1],cluster) plt.scatter(density,sugar_content,c = color)plt.show()

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