在 K-means 聚类中组织聚类

Posted 2023-03-12

【中文标题】在 K-means 聚类中组织聚类

【英文标题】：Organizing Clusters in K-means clustering

【发布时间】：2019-03-16 02:37:51

【问题描述】：

我正在使用 python 对 Mnist 数据库 (http://yann.lecun.com/exdb/mnist/) 进行 k-means 聚类。我能够成功地对数据进行聚类，但无法标记聚类。意思是，我看不到哪个簇号包含哪个数字。例如簇 5 可以容纳数字 7。

在完成 k-means 聚类后，我需要编写代码来正确标记聚类。还需要在代码中添加图例。

from __future__ import division, print_function, absolute_import

import tensorflow as tf 
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D  #only needed for 3D plots 
#scikit learn
from sklearn.cluster import KMeans

#pandas to read excel file
import pandas
import xlrd
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data


Links:
[MNIST Dataset] http://yann.lecun.com/exdb/mnist/




df = pandas.read_csv('test_encoded_with_label.csv',header=None, 
delim_whitespace=True)
#df = pandas.read_excel('test_encoded_with_label.xls')
#print column names
print(df.columns)


df1 = df.iloc[:,0:2] #0 and 1, the last index is not used for iloc
labels = df.iloc[:,2]
labels = labels.values


dataset = df1.values
#train indices - depends how many samples
trainidx = np.arange(0,9999)
testidx = np.arange(0,9999)
train_data = dataset[trainidx,:]
test_data = dataset[testidx,:]
train_labels = labels[trainidx] #just 1D, no :
tpredct_labels = labels[testidx]


kmeans = KMeans(n_clusters=10, random_state=0).fit(train_data)
kmeans.labels_ 
#print(kmeans.labels_.shape)


plt.scatter(train_data[:,0],train_data[:,1], c=kmeans.labels_)


predct_labels = kmeans.predict(train_data)


print(predct_labels)
print('actual label', tpredct_labels)


centers = kmeans.cluster_centers_
print(centers)

plt.show()

【参考方案1】：

要创建标记以查找标记点的集群，您可以使用annotate 方法

这是在 sklearn 数字数据集上运行的示例代码，我尝试在其中标记生成的聚类的质心。请注意，我只是出于说明目的将集群标记为 0-9：

import numpy as np
import matplotlib.pyplot as plt

from sklearn.cluster import KMeans
from sklearn.datasets import load_digits
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale

np.random.seed(42)

digits = load_digits()
data = scale(digits.data)

n_samples, n_features = data.shape
n_digits = len(np.unique(digits.target))
labels = digits.target
h = .02
reduced_data = PCA(n_components=2).fit_transform(data)
kmeans = KMeans(init='k-means++', n_clusters=n_digits, n_init=10)
kmeans.fit(reduced_data)

centroids = kmeans.cluster_centers_
plt_data = plt.scatter(reduced_data[:, 0], reduced_data[:, 1], c=kmeans.labels_, cmap=plt.cm.get_cmap('Spectral', 10))
plt.colorbar()
plt.scatter(centroids[:, 0], centroids[:, 1],
            marker='x')
plt.title('K-means clustering on the digits dataset (PCA-reduced data)\n'
          'Centroids are marked with white cross')
plt.xlabel('component 1')
plt.ylabel('component 2')
labels = ['0'.format(i) for i in range(10)]
for i in range (10):
    xy=(centroids[i, 0],centroids[i, 1])
    plt.annotate(labels[i],xy, horizontalalignment='right', verticalalignment='top')
plt.show()

这是你得到的结果：

【参考方案2】：

要添加图例，try: plt.scatter(train_data[:,0], train_data[:,1], c=kmeans.labels_, label=kmeans.labels_) plt.legend()