single linkage
时间: 2024-05-27 13:10:15 浏览: 5
Single linkage is a clustering method in which clusters are formed by connecting the two closest data points. It is a bottom-up approach, which means it starts with each data point as a separate cluster and then merges the closest pairs of clusters until the desired number of clusters is reached.
In single linkage, the distance between two clusters is defined as the distance between their closest pair of data points. This method tends to produce long, chain-like clusters and is sensitive to noise and outliers. However, it can be effective when dealing with non-convex shapes and irregularly shaped clusters.
相关问题
python实现最短距离single linkage算法
好的,下面是Python实现最短距离single linkage算法的示例代码:
```python
import numpy as np
def single_linkage(X):
"""
:param X: numpy array of shape (n_samples, n_features)
:return: numpy array of shape (n_samples-1, 2)
"""
n = X.shape[0]
clusters = [[i] for i in range(n)]
distances = np.zeros((n, n))
# 计算初始距离矩阵
for i in range(n):
for j in range(i+1, n):
distances[i][j] = distances[j][i] = np.linalg.norm(X[i] - X[j])
# 合并最近的两个簇,直到所有点都在同一个簇中
while len(clusters) > 1:
min_dist = np.inf
for i in range(len(clusters)):
for j in range(i+1, len(clusters)):
for k in clusters[i]:
for l in clusters[j]:
if distances[k][l] < min_dist:
min_dist = distances[k][l]
merge_pair = (i, j)
i, j = merge_pair
new_cluster = clusters[i] + clusters[j]
clusters.pop(j)
clusters[i] = new_cluster
# 更新距离矩阵
for k in range(n):
if k != i and k != j:
distances[i][k] = distances[k][i] = min(distances[k][clusters[i][0]], distances[k][clusters[i][-1]])
return np.array([[clusters[i][0], clusters[i][1]] for i in range(len(clusters)-1)])
```
这里使用了numpy库进行数组操作和计算欧几里得距离。函数接受一个形状为(n_samples, n_features)的numpy数组X, 返回一个形状为(n_samples-1, 2)的numpy数组,其中每行是合并两个簇的点的下标。
linkage python
"linkage" 在 Python 中通常指的是聚类算法中的链接方式(linkage method),用于计算不同簇之间的距离。常见的 linkage 方法包括 single linkage、complete linkage、average linkage 等。
在 Python 中,可以使用 SciPy 库中的 hierarchy 模块来进行聚类分析,其中包括 linkage 函数用于计算不同簇之间的距离。
示例代码:
```python
from scipy.cluster.hierarchy import linkage
import numpy as np
# 生成随机数据
np.random.seed(0)
X = np.random.randn(10, 2)
# 计算距离矩阵
Z = linkage(X, method='single')
# 输出结果
print(Z)
```
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