K-means算法怎么导入乳腺癌数据集？

从sklearn上复制了kmeans相关代码，请问array里的代码改成什么才能绘制出乳腺癌数据集的聚类图像？

from sklearn.datasets import load_breast_cancer
 
from sklearn.neighbors import KNeighborsClassifier
 
from sklearn.model_selection import train_test_split
 
#对数据进行处理
data_breast=load_breast_cancer()
#提取数据的特征
X=data_breast['data']
#提取数据的标签
y=data_breast['target']
#划分数据集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
#数据归一化
from sklearn.preprocessing import MinMaxScaler 
mms=MinMaxScaler()
mms.fit(X_train)
X_train=mms.transform(X_train)
X_test=mms.transform(X_test)
 
#数据交叉验证
from sklearn.model_selection import cross_val_score as csv
from sklearn.neighbors import KNeighborsClassifier
L=[]
for i in range (1,21):
    k=i
    knn=KNeighborsClassifier(n_neighbors=k,weights='distance')
    result=csv(knn,X_train,y_train,cv=5)
    L.append((k,result.mean(),result.var()))
L
 
# 画图观察交叉验证得到的k
a=pd.DataFrame(L)
a.columns=['k','平均准确率','方差']
plt.figure(figsize=(8,6),dpi=100)
plt.plot(a.k,a.平均准确率)
plt.plot(a.k,a.平均准确率+2*a.方差,linestyle='--',color='r')
plt.plot(a.k,a.平均准确率-2*a.方差,linestyle='--',color='r')
plt.xticks(a.k)
plt.xlabel('k')
plt.ylabel('平均准确率')
plt.title('带距离惩罚的5折交叉验证学习曲线')