python密度峰值聚类算法计算聚类中心
时间: 2024-02-02 20:10:39 浏览: 28
Python中的密度峰值聚类算法(Density Peak Clustering)是一种基于密度的聚类算法,它通过计算样本点的局部密度和距离来确定聚类中心。以下是Python中实现密度峰值聚类算法计算聚类中心的步骤:
1. 导入所需的库:
```python
import numpy as np
from sklearn.neighbors import NearestNeighbors
```
2. 定义密度峰值聚类算法的类:
```python
class DensityPeakCluster:
def __init__(self, rho_threshold, delta_threshold):
self.rho_threshold = rho_threshold
self.delta_threshold = delta_threshold
self.density = None
self.delta = None
self.cluster_centers = None
def fit(self, X):
# 计算样本点之间的距离
nbrs = NearestNeighbors(n_neighbors=len(X)).fit(X)
distances, indices = nbrs.kneighbors(X)
# 计算样本点的局部密度
self.density = np.zeros(len(X))
for i in range(len(X)):
self.density[i] = np.sum(distances[i] <= distances[i][-1])
# 计算样本点的最小距离和对应的最近样本点
min_distances = np.min(distances[:, 1:], axis=1)
nearest_indices = indices[:, 1:]
# 计算样本点的相对密度
self.delta = np.zeros(len(X))
for i in range(len(X)):
self.delta[i] = np.min(min_distances[nearest_indices[i]])
# 确定聚类中心
self.cluster_centers = np.where((self.density >= self.rho_threshold) & (self.delta >= self.delta_threshold))[0]
```
3. 使用密度峰值聚类算法计算聚类中心:
```python
# 创建密度峰值聚类算法对象
dpc = DensityPeakCluster(rho_threshold, delta_threshold)
# 计算聚类中心
dpc.fit(X)
# 获取聚类中心的索引
cluster_center_indices = dpc.cluster_centers
```
在上述代码中,`X`是输入的样本数据,`rho_threshold`和`delta_threshold`是用于确定聚类中心的阈值参数。通过调整这两个参数,可以控制聚类的紧密程度。