用python写出鸢尾花聚类分析用肘部法则确定K,并将其结果三维可视化
时间: 2024-02-12 19:10:03 浏览: 84
好的,我可以帮你回答这个问题。
首先,我们需要导入必要的库:
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.cluster import KMeans
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
```
然后,我们可以加载鸢尾花数据集:
```python
iris = load_iris()
data = iris.data
```
接着,我们对数据进行标准化处理:
```python
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data)
```
然后,我们可以使用肘部法则确定最佳的K值:
```python
wcss = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)
kmeans.fit(data_scaled)
wcss.append(kmeans.inertia_)
plt.plot(range(1, 11), wcss)
plt.title('Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.show()
```
最后,我们可以将聚类结果可视化为三维图形:
```python
kmeans = KMeans(n_clusters=3, init='k-means++', max_iter=300, n_init=10, random_state=0)
pred_y = kmeans.fit_predict(data_scaled)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(data_scaled[pred_y == 0, 0], data_scaled[pred_y == 0, 1], data_scaled[pred_y == 0, 2], c='red', marker='o')
ax.scatter(data_scaled[pred_y == 1, 0], data_scaled[pred_y == 1, 1], data_scaled[pred_y == 1, 2], c='blue', marker='o')
ax.scatter(data_scaled[pred_y == 2, 0], data_scaled[pred_y == 2, 1], data_scaled[pred_y == 2, 2], c='green', marker='o')
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.show()
```
这样,我们就完成了鸢尾花聚类分析并将结果可视化为三维图形。
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