我有一个如下所示的数据集：
{＆＃39; dns_query_count＆＃39;：＆＃39; 11＆＃39;，＆＃39; http_hostnames_count＆＃39;：＆＃39; 7＆＃39;，＆＃39; dest_port_count＆＃39;：＆ ＃39; 3＆＃39;，＆＃39; ip_count＆＃39;：＆＃39; 11＆＃39;，＆＃39; signature_count＆＃39;：＆＃39; 0＆＃39;，＆＃39; src_ip＆＃ 39;：＆＃39; 10.0.64.42＆＃39;，＆＃39; http_user_agent_count＆＃39;：＆＃39; 2＆＃39;}
这已经从csv转换为dict
然后我用DictVectorizer转换它
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer()
d = vec.fit_transform(data).toarray()
然后我尝试使用Kmeans
from sklearn.cluster import KMeans
k = KMeans(n_clusters=2).fit(d)
我的问题是如何获得有关哪一行数据属于哪个群集的信息？
我希望得到这样的东西：
{＆＃39; dns_query_count＆＃39;：＆＃39; 11＆＃39;，＆＃39; http_hostnames_count＆＃39;：＆＃39; 7＆＃39;，＆＃39; dest_port_count＆＃39;：＆ ＃39; 3＆＃39;，＆＃39; ip_count＆＃39;：＆＃39; 11＆＃39;，＆＃39; signature_count＆＃39;：＆＃39; 0＆＃39;，＆＃39; src_ip＆＃ 39;：＆＃39; 10.0.64.42＆＃39;，＆＃39; http_user_agent_count＆＃39;：＆＃39; 2＆＃39;，群集：＆＃39; 1＆＃39;}
有人可以给我一步一步示例如何从我展示的原始数据到相同的数据，以及他们所属的集群的信息吗？
例如，我在这个数据集中使用了Weka，它向我展示了我想要的东西 - 我可以点击图表上的数据点并准确读取哪些数据点属于哪个集群。如何用sklearn获得类似的结果？
答案 0 :(得分：2)
这将显示如何检索每行和群集中心的群集ID。我还测量了从每一行到每个质心的距离，以便您可以看到行已正确分配给聚类。
In [1]:
import pandas as pd
from sklearn.cluster import KMeans
from numpy.random import random
from scipy.spatial.distance import euclidean
# I'm going to generate some random data so you can just copy this and see it work
random_data = []
for i in range(0,10):
random_data.append({'dns_query_count': random(),
'http_hostnames_count': random(),
'dest_port_count': random(),
'ip_count': random(),
'signature_count': random(),
'src_ip': random(),
'http_user_agent_count': random()}
)
df = pd.DataFrame(random_data)
km = KMeans(n_clusters=2).fit(df)
df['cluster_id'] = km.labels_
# get the cluster centers and compute the distance from each point to the center
# this will show that all points are assigned to the correct cluster
def distance_to_centroid(row, centroid):
row = row[['dns_query_count',
'http_hostnames_count',
'dest_port_count',
'ip_count',
'signature_count',
'src_ip',
'http_user_agent_count']]
return euclidean(row, centroid)
# to get the cluster centers use km.cluster_centers_
df['distance_to_center0'] = df.apply(lambda r: distance_to_centroid(r,
km.cluster_centers_[0]),1)
df['distance_to_center1'] = df.apply(lambda r: distance_to_centroid(r,
km.cluster_centers_[1]),1)
df.head()
Out [1]:
dest_port_count dns_query_count http_hostnames_count \
0 0.516920 0.135925 0.090209
1 0.528907 0.898578 0.752862
2 0.426108 0.604251 0.524905
3 0.373985 0.606492 0.503487
4 0.319943 0.970707 0.707207
http_user_agent_count ip_count signature_count src_ip cluster_id \
0 0.987878 0.808556 0.860859 0.642014 0
1 0.417033 0.130365 0.067021 0.322509 1
2 0.528679 0.216118 0.041491 0.522445 1
3 0.780292 0.130404 0.048353 0.911599 1
4 0.156117 0.719902 0.484865 0.752840 1
distance_to_center0 distance_to_center1
0 0.846099 1.124509
1 1.175765 0.760310
2 0.970046 0.615725
3 1.054555 0.946233
4 0.640906 1.020849