dbscan计算SSE 可视化分类结果python

以下是使用Python实现DBSCAN算法计算SSE并可视化分类结果的示例代码：

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import DBSCAN
from sklearn.datasets import make_moons

# 生成数据集
X, y = make_moons(n_samples=200, noise=0.05)

# 初始化DBSCAN模型并拟合数据集
dbscan = DBSCAN(eps=0.25, min_samples=5)
dbscan.fit(X)

# 计算SSE
SSE = np.zeros_like(y)
for label in np.unique(dbscan.labels_):
    SSE[dbscan.labels_ == label] = np.sum((X[dbscan.labels_ == label] - np.mean(X[dbscan.labels_ == label], axis=0)) ** 2)

# 可视化分类结果和SSE
fig, ax = plt.subplots(figsize=(10, 6))
ax.scatter(X[dbscan.labels_ == -1, 0], X[dbscan.labels_ == -1, 1], c='gray', label='Noise')
for label in np.unique(dbscan.labels_):
    if label == -1:
        continue
    ax.scatter(X[dbscan.labels_ == label, 0], X[dbscan.labels_ == label, 1], label=f'Cluster {label}')
    ax.annotate(f'SSE: {SSE[dbscan.labels_ == label][0]:.2f}', xy=(np.mean(X[dbscan.labels_ == label, 0]), np.mean(X[dbscan.labels_ == label, 1])), fontsize=10, color='black')
ax.set_xlabel('Feature 1')
ax.set_ylabel('Feature 2')
ax.set_title('DBSCAN Clustering Results')
ax.legend()
plt.show()

运行代码后，将得到一个可视化的分类结果和每个簇的SSE值。如下图所示：