python实现鸢尾花三种聚类算法（K-means,AGNES,DBScan）

import matplotlib.pyplot as plt 
import numpy as np 
from sklearn.cluster import KMeans
from sklearn import datasets 
 
iris = datasets.load_iris() 
X = iris.data[:, :4] # #表示我们取特征空间中的4个维度
print(X.shape)
 
# 绘制数据分布图
plt.scatter(X[:, 0], X[:, 1], c="red", marker='o', label='see') 
plt.xlabel('sepal length') 
plt.ylabel('sepal width') 
plt.legend(loc=2) 
plt.show() 
 
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[:, 0], x0[:, 1], c="red", marker='o', label='label0') 
plt.scatter(x1[:, 0], x1[:, 1], c="green", marker='*', label='label1') 
plt.scatter(x2[:, 0], x2[:, 1], c="blue", marker='+', label='label2') 
plt.xlabel('sepal length') 
plt.ylabel('sepal width') 
plt.legend(loc=2) 
plt.show() 

from sklearn import datasets
from sklearn.cluster import AgglomerativeClustering
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
import pandas as pd
 
iris = datasets.load_iris()
irisdata = iris.data
 
clustering = AgglomerativeClustering(linkage='ward', n_clusters=3)
 
res = clustering.fit(irisdata)
 
print ("各个簇的样本数目：")
print (pd.Series(clustering.labels_).value_counts())
print ("聚类结果：")
print (confusion_matrix(iris.target, clustering.labels_))
 
plt.figure()
d0 = irisdata[clustering.labels_ == 0]
plt.plot(d0[:, 0], d0[:, 1], 'r.')
d1 = irisdata[clustering.labels_ == 1]
plt.plot(d1[:, 0], d1[:, 1], 'go')
d2 = irisdata[clustering.labels_ == 2]
plt.plot(d2[:, 0], d2[:, 1], 'b*')
plt.xlabel("Sepal.Length")
plt.ylabel("Sepal.Width")
plt.title("AGNES Clustering")
plt.show()

import matplotlib.pyplot as plt 
import numpy as np 
from sklearn.cluster import KMeans
from sklearn import datasets 
from sklearn.cluster import DBSCAN
 
iris = datasets.load_iris() 
X = iris.data[:, :4] # #表示我们只取特征空间中的4个维度
print(X.shape)
# 绘制数据分布图
plt.scatter(X[:, 0], X[:, 1], c="red", marker='o', label='see') 
plt.xlabel('sepal length') 
plt.ylabel('sepal width') 
plt.legend(loc=2) 
plt.show() 
 
dbscan = DBSCAN(eps=0.4, min_samples=9)
dbscan.fit(X) 
label_pred = dbscan.labels_
 
# 绘制k-means结果
x0 = X[label_pred == 0]
x1 = X[label_pred == 1]
x2 = X[label_pred == 2]
plt.scatter(x0[:, 0], x0[:, 1], c="red", marker='o', label='label0') 
plt.scatter(x1[:, 0], x1[:, 1], c="green", marker='*', label='label1') 
plt.scatter(x2[:, 0], x2[:, 1], c="blue", marker='+', label='label2') 
plt.xlabel('sepal length') 
plt.ylabel('sepal width') 
plt.legend(loc=2) 
plt.show()