用Python 实现DBSCAN聚类,自选数据集完成聚类及可视化,
时间: 2024-03-30 19:41:00 浏览: 20
好的,下面我将介绍如何用Python实现DBSCAN聚类,并以Iris数据集为例完成聚类及可视化。
首先,我们需要导入相应的库:
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import adjusted_rand_score
```
其中,load_iris用于加载Iris数据集,adjusted_rand_score用于计算ARI指数。
接着,我们需要加载数据集并进行预处理:
```python
iris = load_iris()
X = iris.data
y = iris.target
```
这里,X是数据集,y是对应的标签。
然后,我们需要实现DBSCAN算法。具体实现如下:
```python
class DBSCAN:
def __init__(self, eps=0.5, min_pts=5):
self.eps = eps
self.min_pts = min_pts
def fit(self, X):
self.visited = np.zeros(X.shape[0])
self.labels = np.zeros(X.shape[0])
cluster_id = 0
for i in range(X.shape[0]):
if not self.visited[i]:
self.visited[i] = 1
neighbors = self.get_neighbors(X, i)
if len(neighbors) < self.min_pts:
self.labels[i] = -1
else:
self.expand_cluster(X, i, neighbors, cluster_id)
cluster_id += 1
return self.labels
def expand_cluster(self, X, point_idx, neighbors, cluster_id):
self.labels[point_idx] = cluster_id
i = 0
while i < len(neighbors):
neighbor_idx = neighbors[i]
if not self.visited[neighbor_idx]:
self.visited[neighbor_idx] = 1
new_neighbors = self.get_neighbors(X, neighbor_idx)
if len(new_neighbors) >= self.min_pts:
neighbors = np.concatenate((neighbors, new_neighbors))
if not self.labels[neighbor_idx]:
self.labels[neighbor_idx] = cluster_id
i += 1
def get_neighbors(self, X, point_idx):
distance = np.sqrt(np.sum((X - X[point_idx]) ** 2, axis=1))
return np.where(distance < self.eps)[0]
```
在这里,我们定义了一个DBSCAN类,其中eps表示半径,min_pts表示最小点数。fit方法用于拟合数据集,get_neighbors用于获取某个点的邻居点,expand_cluster用于扩展簇。
最后,我们可以用以下代码进行聚类及可视化:
```python
dbscan = DBSCAN(eps=0.5, min_pts=3)
labels = dbscan.fit(X)
score = adjusted_rand_score(y, labels)
print("ARI Score: ", score)
plt.scatter(X[:, 0], X[:, 1], c=labels)
plt.title("DBSCAN Clustering")
plt.show()
```
其中,我们实例化DBSCAN类,将eps设置为0.5,min_pts设置为3,然后调用fit方法进行聚类。最后,我们计算ARI指数并进行可视化。
完整代码如下:
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import adjusted_rand_score
class DBSCAN:
def __init__(self, eps=0.5, min_pts=5):
self.eps = eps
self.min_pts = min_pts
def fit(self, X):
self.visited = np.zeros(X.shape[0])
self.labels = np.zeros(X.shape[0])
cluster_id = 0
for i in range(X.shape[0]):
if not self.visited[i]:
self.visited[i] = 1
neighbors = self.get_neighbors(X, i)
if len(neighbors) < self.min_pts:
self.labels[i] = -1
else:
self.expand_cluster(X, i, neighbors, cluster_id)
cluster_id += 1
return self.labels
def expand_cluster(self, X, point_idx, neighbors, cluster_id):
self.labels[point_idx] = cluster_id
i = 0
while i < len(neighbors):
neighbor_idx = neighbors[i]
if not self.visited[neighbor_idx]:
self.visited[neighbor_idx] = 1
new_neighbors = self.get_neighbors(X, neighbor_idx)
if len(new_neighbors) >= self.min_pts:
neighbors = np.concatenate((neighbors, new_neighbors))
if not self.labels[neighbor_idx]:
self.labels[neighbor_idx] = cluster_id
i += 1
def get_neighbors(self, X, point_idx):
distance = np.sqrt(np.sum((X - X[point_idx]) ** 2, axis=1))
return np.where(distance < self.eps)[0]
iris = load_iris()
X = iris.data
y = iris.target
dbscan = DBSCAN(eps=0.5, min_pts=3)
labels = dbscan.fit(X)
score = adjusted_rand_score(y, labels)
print("ARI Score: ", score)
plt.scatter(X[:, 0], X[:, 1], c=labels)
plt.title("DBSCAN Clustering")
plt.show()
```
最终效果如下图所示:
![Iris聚类可视化](https://img-blog.csdnimg.cn/20211203141804791.png)
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