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()
Original: https://blog.csdn.net/weixin_39618730/article/details/113981471
Author: weixin_39618730
Title: 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？
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