我想在
python中使用kmeans.
data = [[1,2,3,4,5],[1,0,3,2,4],[4,3,234,5,5],[23,4,5,1,4],[23,5,2,3,5]]
每个数据都有一个标签.例:
[1,2,3,4,5] -> Fiat1
[1,0,3,2,4] -> Fiat2
[4,3,234,5,5] -> Mercedes
[23,4,5,1,4] -> Opel
[23,5,2,3,5] -> bmw
kmeans = KMeans(init='k-means++', n_clusters=3, n_init=10)
kmeans.fit(data)
我的目标是在运行KMeans之后,我想获得每个集群的标签.
一个假的例子:
群集1:
Fiat1,
Fiat2
群集2:
奔驰
群集3:
宝马,
欧宝
我怎样才能做到这一点 ?
最佳答案 码
from sklearn.cluster import KMeans
import numpy as np
data = np.array([[1,2,3,4,5],[1,0,3,2,4],[4,3,234,5,5],[23,4,5,1,4],[23,5,2,3,5]])
labels = np.array(['Fiat1', 'Fiat2', 'Mercedes', 'Opel', 'BMW'])
N_CLUSTERS = 3
kmeans = KMeans(init='k-means++', n_clusters=N_CLUSTERS, n_init=10)
kmeans.fit(data)
pred_classes = kmeans.predict(data)
for cluster in range(N_CLUSTERS):
print('cluster: ', cluster)
print(labels[np.where(pred_classes == cluster)])
输出:
cluster: 0
['Opel' 'BMW']
cluster: 1
['Mercedes']
cluster: 2
['Fiat1' 'Fiat2']