访问由KMeans集群分组的数据的有效方法

Question

我试图在每个质心周围画圆，半径延伸到属于每个星团的最远点。现在，我的圆圈绘制的半径延伸到离群集中心最远的整个训练数据集中的点

这是我的代码：

def KMeansModel(n):
    pca = PCA(n_components=2)
    reduced_train_data = pca.fit_transform(train_data)
    KM = KMeans(n_clusters=n)
    KM.fit(reduced_train_data)
    plt.plot(reduced_train_data[:, 0], reduced_train_data[:, 1], 'k.', markersize=2)
    centroids = KM.cluster_centers_
    # Plot the centroids as a red X
    plt.scatter(centroids[:, 0], centroids[:, 1],
                marker='x', color='r')
    for i in centroids:
        print np.max(metrics.pairwise_distances(i, reduced_train_data))
        plt.gca().add_artist(plt.Circle(i, np.max(metrics.pairwise_distances(i, reduced_train_data)), fill=False))
    plt.show()

out = [KMeansModel(n) for n in np.arange(1,16,1)]

Answer 1

当你这样做时

metrics.pairwise_distances(i, reduced_train_data)

您计算所有训练点的距离，而不仅仅是相关班级的训练点。为了从训练数据中找到与班级ind对应的点的位置，你可以做

np.where(KM.labels_==ind)[0]

因此，在for循环中

for i in centroids:

您需要访问特定班级的培训点。这将完成这项工作：

from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn import metrics
import matplotlib.pyplot as plt
import numpy as np

def KMeansModel(n):
    pca = PCA(n_components=2)
    reduced_train_data = pca.fit_transform(train_data)
    KM = KMeans(n_clusters=n)
    KM.fit(reduced_train_data)
    plt.plot(reduced_train_data[:, 0], reduced_train_data[:, 1], 'k.', markersize=2)
    centroids = KM.cluster_centers_
    # Plot the centroids as a red X
    plt.scatter(centroids[:, 0], centroids[:, 1],
                marker='x', color='r')
    for ind,i in enumerate(centroids):
        class_inds=np.where(KM.labels_==ind)[0]
        max_dist=np.max(metrics.pairwise_distances(i, reduced_train_data[class_inds]))
        print(max_dist)
        plt.gca().add_artist(plt.Circle(i, max_dist, fill=False))
    plt.show()

out = [KMeansModel(n) for n in np.arange(1,16,1)]

以下是我使用代码获得的数据之一：