请问如何通过elbow优化K-means的K取值,以及通过K图优化eps取值,请给出示例代码
时间: 2023-10-08 08:09:27 浏览: 33
首先介绍一下elbow方法用于优化K-means的K取值:
1. 导入需要的库
```
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
```
2. 生成数据
```
X, y = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)
plt.scatter(X[:,0], X[:,1])
plt.show()
```
3. 执行K-means算法,建立多个模型
```
inertias = []
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(X)
inertias.append(kmeans.inertia_)
```
4. 绘制K值与SSE值的曲线图
```
plt.plot(range(1, 11), inertias, marker='o')
plt.xlabel('Number of clusters')
plt.ylabel('SSE')
plt.show()
```
5. 通过观察曲线图确定最优的K值
接下来是K图方法用于优化DBSCAN算法的eps取值:
1. 导入需要的库
```
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons
```
2. 生成数据
```
X, y = make_moons(n_samples=200, noise=0.05, random_state=0)
X = StandardScaler().fit_transform(X)
plt.scatter(X[:,0], X[:,1])
plt.show()
```
3. 执行DBSCAN算法,建立多个模型
```
dbscan = DBSCAN(eps=0.3, min_samples=5)
clusters = dbscan.fit_predict(X)
```
4. 绘制K图
```
distances = np.sort(dbscan.distances_[dbscan.distances_ != 0])
distances = distances.reshape(-1, 1)
k = np.arange(len(distances))
plt.plot(k, distances)
plt.xlabel('Number of points')
plt.ylabel('Distance')
plt.show()
```
5. 通过观察K图确定最优的eps值
希望这个示例代码能够帮到你。