我在学习K-means聚类。对plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')的工作原理感到非常困惑，代码中的X[y_kmeans == 0, 0], X[y_kmeans == 0, 1]的目的是什么？

完整代码如下

#k-means#importing librariesimport numpy as npimport matplotlib.pyplot as pltimport pandas as pd#importing the datasetdataset = pd.read_csv("mall_customers.csv")X = dataset.iloc[:,[3,4]].values#using the elbow method to find the optimal number of clustersfrom sklearn.cluster import KMeanswcss = [] #Within-Cluster Sum of Squarefor i in range(1,11):    kmeans = KMeans(n_clusters = i, init = 'k-means++',max_iter = 300,n_init=10,random_state = 0)    kmeans.fit(X)    wcss.append(kmeans.inertia_)plt.plot(range(1,11),wcss)plt.title("The elbow method")plt.xlabel("Number of cluster")plt.ylabel('Wcss') plt.show()    #applying kmeans to all datasetkmeans = KMeans(n_clusters = 5,init = 'k-means++', max_iter=300,n_init=10,random_state=0)y_kmeans = kmeans.fit_predict(X)#Visualising the clusterplt.scatter(X[y_kmeans == 0,0],X[y_kmeans == 0,1],s=100,c = 'red' ,label='Cluster1')plt.scatter(X[y_kmeans == 1,0],X[y_kmeans == 1,1],s=100,c='blue', label='Cluster2')plt.scatter(X[y_kmeans == 2,0],X[y_kmeans == 2,1],s=100,c='green',label='Cluster3')plt.scatter(X[y_kmeans == 3,0],X[y_kmeans == 3,1],s=100, c ='cyan',label = 'CLuster4')plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')plt.scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],s=300, c = 'yellow', label ='Centroids')plt.title('Clusters of customers')plt.xlabel('Annual Income (k$)')plt.ylabel('Spending Score (1-100)')plt.legend()plt.show()

我添加了输出图像供参考肘部图,最终聚类图像

回答：

这是一个过滤器。y_kmeans == 0选择那些y_kmeans[i]等于0的元素。X[y_kmeans == 0, 0]选择X中对应的y_kmeans值为0且第二维度为0的元素。

最初由@***回答

X[y_hc ==1,0]这里的0表示模型在x平面，X[y_hc == 0,1]表示模型在y平面。而1指的是[i]的值或聚类值。