聚类模型-K均值、DBSCAN、AGENS_如何看agens聚类分析图-优快云博客

该代码示例展示了如何使用Python的sklearn库进行三种不同的聚类算法——KMeans、DBSCAN和AGNES（AgglomerativeClustering）。在这三个例子中，都使用了鸢尾花数据集，并通过可视化展示不同聚类结果，包括红色、绿色和蓝色表示的不同类别。

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1、K均值聚类模型

import matplotlib.pyplot as plt
import numpy as np 
from sklearn.cluster import KMeans
from sklearn.datasets import load_iris
X = load_iris() .data #表示我们取特征空间中的4个维度
estimator = KMeans(n_clusters=3) # 构造聚类器
estimator.fit(X) # 聚类
label_pred = estimator.labels_ # 获取聚类标签
# 绘制k-means结果
x0 = X[label_pred == 0]
x1 = X[label_pred == 1]
x2 = X[label_pred == 2]
plt.scatter(x0[:, 2], x0[:, 3], c="red", marker='o', label='label0') 
plt.scatter(x1[:, 2], x1[:, 3], c="green", marker='*', label='label1') 
plt.scatter(x2[:, 2], x2[:, 3], c="blue", marker='+', label='label2') 
plt.legend(loc=2) 
plt.show()

2、DBSCAN聚类模型

import matplotlib.pyplot as plt
import numpy as np 
from sklearn.datasets import load_iris
from sklearn.cluster import DBSCAN
X = load_iris() .data
dbscan = DBSCAN(eps=0.4, min_samples=9)
dbscan.fit(X) 
label_pred = dbscan.labels_
x0 = X[label_pred == 0]
x1 = X[label_pred == 1]
x2 = X[label_pred == 2]
plt.scatter(x0[:, 2], x0[:, 3], c="red", marker='o', label='label0') 
plt.scatter(x1[:, 2], x1[:, 3], c="green", marker='*', label='label1') 
plt.scatter(x2[:, 2], x2[:, 3], c="blue", marker='+', label='label2') 
plt.legend(loc=2) 
plt.show()

2、AGENS聚类模型

import matplotlib.pyplot as plt
import numpy as np 
from sklearn.datasets import load_iris
from sklearn.cluster import AgglomerativeClustering
X = load_iris().data 
agnes = AgglomerativeClustering(linkage='ward', n_clusters=3)
agnes.fit(X) 
label_pred = agnes.labels_
x0 = X[label_pred == 0]
x1 = X[label_pred == 1]
x2 = X[label_pred == 2]
plt.scatter(x0[:, 2], x0[:, 3], c="red", marker='o', label='label0') 
plt.scatter(x1[:, 2], x1[:, 3], c="green", marker='*', label='label1') 
plt.scatter(x2[:, 2], x2[:, 3], c="blue", marker='+', label='label2') 
plt.legend(loc=2) 
plt.show()