python怎么写入聚类标签_如何从k均值聚类算法中生成聚类的输出标签？

我的输入特性集是csv文件的形式。我用过

http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html

在我的未标记数据上选择簇数，并且我得到的是cluster size=3的最高分数

因此我的簇=3。有没有一种方法可以为kmeans聚类算法的每个输入行生成输出标签？在

基本上我想打印出kmeans算法的所有输入特性和结果(集群标签)

Ie我想打印出所有输入特性和分配给每行输入csv文件的集群标签。在

python代码import pandas

from pandas import read_csv

names = ["Variable1”, "Variable2”, "Variable3”, "Variable4”, "Variable5”, "Variable6”, "Variable7”, "Variable8”, "Variable9”, "Variable10”, "Variable11”, "Variable12”, "InputClass”]

filename = 'C:/Users/svx/d_features.csv'

dataframe = read_csv(filename, names=names)

array = dataframe.values

X = array[:,2:11]

range_n_clusters = [2, 3, 4, 5, 6]

for n_clusters in range_n_clusters:

# Create a subplot with 1 row and 2 columns

fig, (ax1, ax2) = plt.subplots(1, 2)

fig.set_size_inches(18, 7)

# The 1st subplot is the silhouette plot

# The silhouette coefficient can range from -1, 1 but in this example all

# lie within [-0.1, 1]

ax1.set_xlim([-0.1, 1])

# The (n_clusters+1)*10 is for inserting blank space between silhouette

# plots of individual clusters, to demarcate them clearly.

ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])

# Initialize the clusterer with n_clusters value and a random generator

# seed of 10 for reproducibility.

clusterer = KMeans(n_clusters=n_clusters, random_state=10)

cluster_labels = clusterer.fit_predict(X)

# The silhouette_score gives the average value for all the samples.

# This gives a perspective into the density and separation of the formed

# clusters

silhouette_avg = silhouette_score(X, cluster_labels)

print("For n_clusters =", n_clusters,

"The average silhouette_score is :", silhouette_avg)

# Compute the silhouette scores for each sample

sample_silhouette_values = silhouette_samples(X, cluster_labels)

y_lower = 10

for i in range(n_clusters):

# Aggregate the silhouette scores for samples belonging to

# cluster i, and sort them

ith_cluster_silhouette_values = \

sample_silhouette_values[cluster_labels == i]

ith_cluster_silhouette_values.sort()

size_cluster_i = ith_cluster_silhouette_values.shape[0]

y_upper = y_lower + size_cluster_i

color = cm.spectral(float(i) / n_clusters)

ax1.fill_betweenx(np.arange(y_lower, y_upper),

0, ith_cluster_silhouette_values,

facecolor=color, edgecolor=color, alpha=0.7)

# Label the silhouette plots with their cluster numbers at the middle

ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))

# Compute the new y_lower for next plot

y_lower = y_upper + 10 # 10 for the 0 samples

ax1.set_title("The silhouette plot for the various clusters.")

ax1.set_xlabel("The silhouette coefficient values")

ax1.set_ylabel("Cluster label")

# The vertical line for average silhouette score of all the values

ax1.axvline(x=silhouette_avg, color="red", linestyle="--")

ax1.set_yticks([]) # Clear the yaxis labels / ticks

ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])

# 2nd Plot showing the actual clusters formed

colors = cm.spectral(cluster_labels.astype(float) / n_clusters)

ax2.scatter(X[:, 0], X[:, 1], marker='.', s=30, lw=0, alpha=0.7,

c=colors)

# Labeling the clusters

centers = clusterer.cluster_centers_

# Draw white circles at cluster centers

ax2.scatter(centers[:, 0], centers[:, 1],

marker='o', c="white", alpha=1, s=200)

for i, c in enumerate(centers):

ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1, s=50)

ax2.set_title("The visualization of the clustered data.")

ax2.set_xlabel("Feature space for the 1st feature")

ax2.set_ylabel("Feature space for the 2nd feature")

plt.suptitle(("Silhouette analysis for KMeans clustering on sample data "

"with n_clusters = %d" % n_clusters),

fontsize=14, fontweight='bold')

plt.show()