python - 聚类后​​的 TSNE 图

Question

我对数据应用了 K_Mean 聚类，并在应用 TSNE 绘制数据之后。我有 4 个维度和 4 个组。问题是我的 K_mean 是正确的，但为什么使用 tsne，同一组不在一起？

the code : 

XX = df [["agent_os_new","agent_category_new","referer_new","agent_name_new"]]

y = df['referer_new']
y
cols = XX.columns

from sklearn.preprocessing import MinMaxScaler

ms = MinMaxScaler()

X = ms.fit_transform(XX)

X = pd.DataFrame(X, columns=[cols])
X[:4]


from sklearn.cluster import KMeans

kmeans = KMeans(n_clusters=4, random_state=0) 

ymeans = kmeans.fit(X)

ymeans


labels = kmeans.labels_

df_new = XX.assign(Cluster =labels)
df_new



from sklearn.manifold import TSNE
import seaborn as sns

X_embedded = TSNE(n_components=2).fit_transform(df_new)

df_subset = pd.DataFrame()
df_subset['tsne1'] = X_embedded[:,0]
df_subset['tsne2'] = X_embedded[:,1]

plt.figure(figsize=(16,10))
sns.scatterplot(
    x="tsne1", y="tsne2",
    hue=df.label,
    palette="Set1",
    data=df_subset,
    style=df_new["Cluster"],
    legend="full",
    s=120
)

我想要的是：

Answer 1

from sklearn.manifold import TSNE
import seaborn as sns

X_embedded = TSNE(n_components=2,random_state=42).fit_transform(X)
centers = np.array(kmeans.cluster_centers_)
model = KMeans(n_clusters = 4, init = "k-means++")
label = model.fit_predict(X_embedded)


plt.figure(figsize=(10,10))
uniq = np.unique(label)
for i in uniq:
   plt.scatter(data[label == i , 0] , data[label == i , 1] , label = i)
plt.scatter(centers[:,0], centers[:,1], marker="x", color='k')
#This is done to find the centroid for each clusters.
plt.legend()
plt.show()