基于鸢尾花数据集的四种聚类算法（kmeans，层次聚类，DBSCAN,FCM）和学习向量量化对比
注：下面的代码可能需要做一点参数调整，才得到所有我的运行结果。
kmeans算法：
python
import matplotlib.pyplot as plt # 导入matplotlib的库
import numpy as np # 导入numpy的包
from sklearn import datasets #导入数据集
from sklearn.decomposition import PCA # PCA主成分分析类
from sklearn.metrics import silhouette_score
from sklearn.metrics import calinski_harabasz_score
from sklearn.metrics import davies_bouldin_score
iris = datasets.load_iris() #加载iris数据集
X = iris.data #加载特征数据
# Y = iris.target #加载标签数据
#绘制数据分布图
y = iris.target
X = iris.data
#X.shape
#调用PCA
pca = PCA(n_components=2) # 降到2维
pca = pca.fit(X) #拟合模型
X_dr = pca.transform(X) #获取新矩阵 （降维后的）
#X_dr
#也可以fit_transform一步到位
#X_dr = PCA(2).fit_transform(X)
#plt.figure()
#plt.scatter(X_dr[y==0, 0], X_dr[y==0, 1], c="red", label=iris.target_names[0])
#plt.scatter(X_dr[y==1, 0], X_dr[y==1, 1], c="black", label=iris.target_names[1])
#plt.scatter(X_dr[y==2, 0], X_dr[y==2, 1], c="orange", label=iris.target_names[2])
#plt.legend()
#plt.title('PCA of IRIS dataset')
#plt.show()
print("===K-means聚类===")
from sklearn.cluster import KMeans # 引入KMeans模块
estimator = KMeans(n_clusters=3).fit(X) # 构造聚类器
label_pred = estimator.labels_ # 获取聚类标签
# 评估指标列表
silhouette_avg_scores = []
Calinski_Harabasz_scores = []
Davies_Bouldin_scores = []
# 遍历不同的n_clusters值
for n_clusters in range(2, 11):
kmeans = KMeans(n_clusters=n_clusters)
kmeans.fit(X)
labels = kmeans.labels_
silhouette_avg = silhouette_score(X, kmeans.labels_)
print(silhouette_avg)
# 2. Calinski-Harabasz指数
calinski_haraba=calinski_harabasz_score(X, kmeans.labels_)
print(calinski_haraba)
# 3. DB指数（Davies-Bouldin Index）
davies_bouldin=davies_bouldin_score(X, kmeans.labels_)
Davies_Bouldin_scores.append(davies_bouldin)
Calinski_Harabasz_scores.append(calinski_haraba)
silhouette_avg_scores.append(silhouette_avg)
# 绘制图形
plt.plot(range(2, 11), silhouette_avg_scores, marker='o', label='Silhouette Coefficient')
plt.title('Silhouette Coefficient for Different n_clusters-kmeans')
plt.xlabel('Number of clusters (n_clusters)')
plt.ylabel('Silhouette Coefficient')
plt.legend()
plt.show()
plt.plot(range(2, 11), Calinski_Harabasz_scores, marker='o', label=' Calinski-Harabasz')
plt.title(' Calinski-Harabaszfor Different n_clusters-kmeans')
plt.xlabel('Number of clusters (n_clusters)')
plt.ylabel('Calinski Harabasz')
plt.legend()
plt.show()
plt.plot(range(2, 11), Davies_Bouldin_scores, marker='o', label='Davies-Bouldin Index')
plt.title('Davies-Bouldin Index for Different n_clusters-kmeans')
plt.xlabel('Number of clusters (n_clusters)')
plt.ylabel('Davies-Bouldin Index')
plt.legend()
plt.show()运行结果如下：
DBSCAN:
python
import matplotlib.pyplot as plt # 导入matplotlib的库
import numpy as np # 导入numpy的包
from sklearn import datasets #导入数据集
from sklearn.decomposition import PCA # PCA主成分分析类
from sklearn.metrics import silhouette_score
from sklearn.metrics import calinski_harabasz_score
from sklearn.metrics import davies_bouldin_score
from sklearn.cluster import DBSCAN # 引入DBSCAN模块
iris = datasets.load_iris() #加载iris数据集
X = iris.data #加载特征数据
# Y = iris.target #加载标签数据
#绘制数据分布图
y = iris.target
X = iris.data
#X.shape
##调用PCA
#pca = PCA(n_components=2) # 降到2维
#pca = pca.fit(X) #拟合模型
#X_dr = pca.transform(X) #获取新矩阵 （降维后的）
##X_dr
#也可以fit_transform一步到位
#X_dr = PCA(2).fit_transform(X)
#plt.figure()
#plt.scatter(X_dr[y==0, 0], X_dr[y==0, 1], c="red", label=iris.target_names[0])
#plt.scatter(X_dr[y==1, 0], X_dr[y==1, 1], c="black", label=iris.target_names[1])
#plt.scatter(X_dr[y==2, 0], X_dr[y==2, 1], c="orange", label=iris.target_names[2])
#plt.legend()
#plt.title('PCA of IRIS dataset')
#plt.show()
print("===DBSCAN聚类===")
from sklearn.cluster import KMeans # 引入KMeans模块
estimator = KMeans(n_clusters=3).fit(X) # 构造聚类器
label_pred = estimator.labels_ # 获取聚类标签
# 评估指标列表
silhouette_avg_scores = []
Calinski_Harabasz_scores = []
Davies_Bouldin_scores = []
# 遍历不同的n_clusters值
for n_clusters in range(2, 11):
dbscan = DBSCAN(eps=0.4, min_samples=n_clusters).fit(X) #导入DBSCAN模块进行训练,在一个邻域的半径内min_samples数的邻域eps被认为是一个簇。请记住，初始点包含在min_samples中。
label_pred = dbscan.labels_ # labels为每个数据的簇标签,不在任何"高密度"集群中的"noisy"样本返回-1
silhouette_avg = silhouette_score(X, dbscan.labels_)
print(silhouette_avg)
# 2. Calinski-Harabasz指数
calinski_haraba=calinski_harabasz_score(X, dbscan.labels_)
print(calinski_haraba)
# 3. DB指数（Davies-Bouldin Index）
davies_bouldin=davies_bouldin_score(X, dbscan.labels_)
Davies_Bouldin_scores.append(davies_bouldin)
Calinski_Harabasz_scores.append(calinski_haraba)
silhouette_avg_scores.append(silhouette_avg)
# 绘制图形
plt.plot(range(2, 11), silhouette_avg_scores, marker='o', label='Silhouette Coefficient')
plt.title('Silhouette Coefficient for Different min_samples-DBSCAN-eps=0.4')
plt.xlabel('Number of min_samples (min_samples)')
plt.ylabel('Silhouette Coefficient')
plt.legend()
plt.show()
plt.plot(range(2, 11), Calinski_Harabasz_scores, marker='o', label=' Calinski-Harabasz')
plt.title('Calinski-Harabasz for Different min_samples-DBSCAN-eps=0.4')
plt.xlabel('Number of min_samples (min_samples)')
plt.ylabel('Calinski Harabasz')
plt.legend()
plt.show()
plt.plot(range(2, 11), Davies_Bouldin_scores, marker='o', label='Davies-Bouldin Index')
plt.title('Davies-Bouldin Index for Different min_samples-DBSCAN-eps=0.4')
plt.xlabel('Number of min_samples (min_samples)')
plt.ylabel('Davies-Bouldin Index')
plt.legend()
plt.show()
# 评估指标列表
silhouette_avg_scores = []
Calinski_Harabasz_scores = []
Davies_Bouldin_scores = []
xindex= [0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.4,1.6]
for s in xindex:
print(s)
dbscan = DBSCAN(eps=s, min_samples=3).fit(X) #导入DBSCAN模块进行训练,在一个邻域的半径内min_samples数的邻域eps被认为是一个簇。请记住，初始点包含在min_samples中。
label_pred = dbscan.labels_ # labels为每个数据的簇标签,不在任何"高密度"集群中的"noisy"样本返回-1
silhouette_avg = silhouette_score(X, dbscan.labels_)
print(silhouette_avg)
# 2. Calinski-Harabasz指数
calinski_haraba=calinski_harabasz_score(X, dbscan.labels_)
print(calinski_haraba)
# 3. DB指数（Davies-Bouldin Index）
davies_bouldin=davies_bouldin_score(X, dbscan.labels_)
Davies_Bouldin_scores.append(davies_bouldin)
Calinski_Harabasz_scores.append(calinski_haraba)
silhouette_avg_scores.append(silhouette_avg)
# 绘制图形
plt.plot(xindex, silhouette_avg_scores, marker='o', label='Silhouette Coefficient')
plt.title('Silhouette Coefficient for Different min_samples-DBSCAN- min_samples=3')
plt.xlabel('eps')
plt.ylabel('Silhouette Coefficient')
plt.legend()
plt.show()
plt.plot(xindex, Calinski_Harabasz_scores, marker='o', label=' Calinski-Harabasz')
plt.title('Calinski-Harabasz for Different min_samples-DBSCAN- min_samples=3')
plt.xlabel('eps')
plt.ylabel('Calinski Harabasz')
plt.legend()
plt.show()
plt.plot(xindex, Davies_Bouldin_scores, marker='o', label='Davies-Bouldin Index')
plt.title('Davies-Bouldin Index for Different min_samples-DBSCAN- min_samples=3')
plt.xlabel('eps')
plt.ylabel('Davies-Bouldin Index')
plt.legend()
plt.show()运行结果：
层次聚类：
python
import matplotlib.pyplot as plt # 导入matplotlib的库
import numpy as np # 导入numpy的包
from sklearn import datasets #导入数据集
from sklearn.decomposition import PCA # PCA主成分分析类
from sklearn.metrics import silhouette_score
from sklearn.metrics import calinski_harabasz_score
from sklearn.metrics import davies_bouldin_score
from sklearn.cluster import AgglomerativeClustering
iris = datasets.load_iris() #加载iris数据集
X = iris.data #加载特征数据
# Y = iris.target #加载标签数据
#绘制数据分布图
y = iris.target
X = iris.data
#X.shape
#调用PCA
pca = PCA(n_components=2) # 降到2维
pca = pca.fit(X) #拟合模型
X_dr = pca.transform(X) #获取新矩阵 （降维后的）
#X_dr
#也可以fit_transform一步到位
#X_dr = PCA(2).fit_transform(X)
#plt.figure()
#plt.scatter(X_dr[y==0, 0], X_dr[y==0, 1], c="red", label=iris.target_names[0])
#plt.scatter(X_dr[y==1, 0], X_dr[y==1, 1], c="black", label=iris.target_names[1])
#plt.scatter(X_dr[y==2, 0], X_dr[y==2, 1], c="orange", label=iris.target_names[2])
#plt.legend()
#plt.title('PCA of IRIS dataset')
#plt.show()
print("===K-means聚类===")
from sklearn.cluster import KMeans # 引入KMeans模块
estimator = KMeans(n_clusters=3).fit(X) # 构造聚类器
label_pred = estimator.labels_ # 获取聚类标签
# 评估指标列表
silhouette_avg_scores = []
Calinski_Harabasz_scores = []
Davies_Bouldin_scores = []
# 遍历不同的n_clusters值
for n_clusters in range(2, 11):
agg = AgglomerativeClustering( n_clusters=n_clusters)
agg.fit(X)
labels = agg.labels_
silhouette_avg = silhouette_score(X, agg.labels_)
# 2. Calinski-Harabasz指数
calinski_haraba=calinski_harabasz_score(X, agg.labels_)
# 3. DB指数（Davies-Bouldin Index）
davies_bouldin=davies_bouldin_score(X, agg.labels_)
Davies_Bouldin_scores.append(davies_bouldin)
Calinski_Harabasz_scores.append(calinski_haraba)
silhouette_avg_scores.append(silhouette_avg)
# 绘制图形
plt.plot(range(2, 11), silhouette_avg_scores, marker='o', label='Silhouette Coefficient')
plt.title('Silhouette Coefficient for Different n_clusters-AgglomerativeClustering')
plt.xlabel('Number of clusters (n_clusters)')
plt.ylabel('Silhouette Coefficient')
plt.legend()
plt.show()
plt.plot(range(2, 11), Calinski_Harabasz_scores, marker='o', label=' Calinski-Harabasz')
plt.title(' Calinski-Harabaszfor Different n_clusters-AgglomerativeClustering')
plt.xlabel('Number of clusters (n_clusters)')
plt.ylabel('Calinski Harabasz')
plt.legend()
plt.show()
plt.plot(range(2, 11), Davies_Bouldin_scores, marker='o', label='Davies-Bouldin Index')
plt.title('Davies-Bouldin Index for Different n_clusters-AgglomerativeClustering')
plt.xlabel('Number of clusters (n_clusters)')
plt.ylabel('Davies-Bouldin Index')
plt.legend()
plt.show()运行结果：
FCM算法：
代码：
python
import matplotlib.pyplot as plt # 导入matplotlib的库
import numpy as np # 导入numpy的包
from sklearn import datasets #导入数据集
from sklearn.decomposition import PCA # PCA主成分分析类
from sklearn.metrics import silhouette_score
from sklearn.metrics import calinski_harabasz_score
from sklearn.metrics import davies_bouldin_score
from sklearn.cluster import FeatureAgglomeration
from sklearn.cluster import AgglomerativeClustering
iris = datasets.load_iris() #加载iris数据集
X = iris.data #加载特征数据
# Y = iris.target #加载标签数据
#绘制数据分布图
y = iris.target
X = iris.data
#X.shape
#调用PCA
pca = PCA(n_components=2) # 降到2维
pca = pca.fit(X) #拟合模型
X_dr = pca.transform(X) #获取新矩阵 （降维后的）
#X_dr
#也可以fit_transform一步到位
#X_dr = PCA(2).fit_transform(X)
#plt.figure()
#plt.scatter(X_dr[y==0, 0], X_dr[y==0, 1], c="red", label=iris.target_names[0])
#plt.scatter(X_dr[y==1, 0], X_dr[y==1, 1], c="black", label=iris.target_names[1])
#plt.scatter(X_dr[y==2, 0], X_dr[y==2, 1], c="orange", label=iris.target_names[2])
#plt.legend()
#plt.title('PCA of IRIS dataset')
#plt.show()
print("===K-means聚类===")
from sklearn.cluster import KMeans # 引入KMeans模块
def FCM(X, c_clusters=3, m=2, eps=10):
membership_mat = np.random.random((len(X), c_clusters)) # 生成随机二维数组shape(150,3)，随机初始化隶属矩阵
# 这一步的操作是为了使Xi的隶属度总和为1
membership_mat = np.divide(membership_mat, np.sum(membership_mat, axis=1)[:, np.newaxis])
while True:
working_membership_mat = membership_mat ** m # shape->(150,3)
# 根据公式计算聚类中心点Centroids.shape->(3,4)
Centroids = np.divide(np.dot(working_membership_mat.T, X), np.sum(working_membership_mat.T, axis=1)[:, np.newaxis])
# 该矩阵保存所有实点到每个聚类中心的欧式距离
n_c_distance_mat = np.zeros((len(X), c_clusters)) # shape->(150,3)
for i, x in enumerate(X):
for j, c in enumerate(Centroids):
n_c_distance_mat[i][j] = np.linalg.norm(x-c, 2) # 计算l2范数(欧氏距离)
new_membership_mat = np.zeros((len(X), c_clusters))
# 根据公式计算模糊矩阵U
for i, x in enumerate(X):
for j, c in enumerate(Centroids):
new_membership_mat[i][j] = 1. / np.sum((n_c_distance_mat[i][j] / n_c_distance_mat[i]) ** (2 / (m-1)))
if np.sum(abs(new_membership_mat - membership_mat)) < eps:
break
membership_mat = new_membership_mat
return np.argmax(new_membership_mat, axis=1)
# 评估指标列表
silhouette_avg_scores = []
Calinski_Harabasz_scores = []
Davies_Bouldin_scores = []
# 遍历不同的n_clusters值
for n_clusters in range(2, 11):
print(n_clusters)
fcm =FCM(X, c_clusters=n_clusters)
print(len(fcm ))
silhouette_avg = silhouette_score(X, fcm)
print(silhouette_avg)
# 2. Calinski-Harabasz指数
calinski_haraba=calinski_harabasz_score(X, fcm)
print(calinski_haraba)
# 3. DB指数（Davies-Bouldin Index）
davies_bouldin=davies_bouldin_score(X,fcm)
Davies_Bouldin_scores.append(davies_bouldin)
Calinski_Harabasz_scores.append(calinski_haraba)
silhouette_avg_scores.append(silhouette_avg)
# 绘制图形
plt.plot(range(2, 11), silhouette_avg_scores, marker='o', label='Silhouette Coefficient')
plt.title('Silhouette Coefficient for Different n_clusters-FCM')
plt.xlabel('Number of clusters (n_clusters)')
plt.ylabel('Silhouette Coefficient')
plt.legend()
plt.show()
plt.plot(range(2, 11), Calinski_Harabasz_scores, marker='o', label=' Calinski-Harabasz')
plt.title(' Calinski-Harabaszfor Different n_clusters-FCM')
plt.xlabel('Number of clusters (n_clusters)')
plt.ylabel('Calinski Harabasz')
plt.legend()
plt.show()
plt.plot(range(2, 11), Davies_Bouldin_scores, marker='o', label='Davies-Bouldin Index')
plt.title('Davies-Bouldin Index for Different n_clusters-FCM')
plt.xlabel('Number of clusters (n_clusters)')
plt.ylabel('Davies-Bouldin Index')
plt.legend()
plt.show()
lvp算法：
python
import matplotlib.pyplot as plt # 导入matplotlib的库
import numpy as np # 导入numpy的包
from sklearn import datasets #导入数据集
from sklearn.decomposition import PCA # PCA主成分分析类
from sklearn.metrics import silhouette_score
from sklearn.metrics import calinski_harabasz_score
from sklearn.metrics import davies_bouldin_score
from sklearn.cluster import FeatureAgglomeration
from sklearn.cluster import AgglomerativeClustering
# 使用LVQ进行聚类
from sklearn_lvq import GlvqModel
iris = datasets.load_iris() #加载iris数据集
X = iris.data #加载特征数据
# Y = iris.target #加载标签数据
#绘制数据分布图
y = iris.target
X = iris.data
#X.shape
#调用PCA
pca = PCA(n_components=2) # 降到2维
pca = pca.fit(X) #拟合模型
X_dr = pca.transform(X) #获取新矩阵 （降维后的）
#X_dr
#也可以fit_transform一步到位
#X_dr = PCA(2).fit_transform(X)
#plt.figure()
#plt.scatter(X_dr[y==0, 0], X_dr[y==0, 1], c="red", label=iris.target_names[0])
#plt.scatter(X_dr[y==1, 0], X_dr[y==1, 1], c="black", label=iris.target_names[1])
#plt.scatter(X_dr[y==2, 0], X_dr[y==2, 1], c="orange", label=iris.target_names[2])
#plt.legend()
#plt.title('PCA of IRIS dataset')
#plt.show()
def FCM(X, c_clusters=3, m=2, eps=10):
membership_mat = np.random.random((len(X), c_clusters)) # 生成随机二维数组shape(150,3)，随机初始化隶属矩阵
# 这一步的操作是为了使Xi的隶属度总和为1
membership_mat = np.divide(membership_mat, np.sum(membership_mat, axis=1)[:, np.newaxis])
while True:
working_membership_mat = membership_mat ** m # shape->(150,3)
# 根据公式计算聚类中心点Centroids.shape->(3,4)
Centroids = np.divide(np.dot(working_membership_mat.T, X), np.sum(working_membership_mat.T, axis=1)[:, np.newaxis])
# 该矩阵保存所有实点到每个聚类中心的欧式距离
n_c_distance_mat = np.zeros((len(X), c_clusters)) # shape->(150,3)
for i, x in enumerate(X):
for j, c in enumerate(Centroids):
n_c_distance_mat[i][j] = np.linalg.norm(x-c, 2) # 计算l2范数(欧氏距离)
new_membership_mat = np.zeros((len(X), c_clusters))
# 根据公式计算模糊矩阵U
for i, x in enumerate(X):
for j, c in enumerate(Centroids):
new_membership_mat[i][j] = 1. / np.sum((n_c_distance_mat[i][j] / n_c_distance_mat[i]) ** (2 / (m-1)))
if np.sum(abs(new_membership_mat - membership_mat)) < eps:
break
membership_mat = new_membership_mat
return np.argmax(new_membership_mat, axis=1)
# 评估指标列表
silhouette_avg_scores = []
Calinski_Harabasz_scores = []
Davies_Bouldin_scores = []
from sklearn.datasets import make_blobs
# 遍历不同的n_clusters值
for n_clusters in range(2, 11):
print(n_clusters)
zX, y_true = make_blobs(n_samples=150, centers=n_clusters, cluster_std=0.6, random_state=0)
lvq = GlvqModel()
lvq.fit(X, y_true)
# 可视化聚类结果
fcm = lvq.predict(X)
print(len(fcm ))
silhouette_avg = silhouette_score(X, fcm)
print(silhouette_avg)
# 2. Calinski-Harabasz指数
calinski_haraba=calinski_harabasz_score(X, fcm)
print(calinski_haraba)
# 3. DB指数（Davies-Bouldin Index）
davies_bouldin=davies_bouldin_score(X,fcm)
Davies_Bouldin_scores.append(davies_bouldin)
Calinski_Harabasz_scores.append(calinski_haraba)
silhouette_avg_scores.append(silhouette_avg)
# 绘制图形
plt.plot(range(2, 11), silhouette_avg_scores, marker='o', label='Silhouette Coefficient')
plt.title('Silhouette Coefficient for Different n_clusters--lvp')
plt.xlabel('Number of clusters (n_clusters)')
plt.ylabel('Silhouette Coefficient')
plt.legend()
plt.show()
plt.plot(range(2, 11), Calinski_Harabasz_scores, marker='o', label=' Calinski-Harabasz')
plt.title(' Calinski-Harabaszfor Different n_clusters--lvp')
plt.xlabel('Number of clusters (n_clusters)')
plt.ylabel('Calinski Harabasz')
plt.legend()
plt.show()
plt.plot(range(2, 11), Davies_Bouldin_scores, marker='o', label='Davies-Bouldin Index')
plt.title('Davies-Bouldin Index for Different n_clusters-lvp')
plt.xlabel('Number of clusters (n_clusters)')
plt.ylabel('Davies-Bouldin Index')
plt.legend()
plt.show()
最后我们还做了一个所有算法最优参数汇总的代码：
python
import matplotlib.pyplot as plt # 导入matplotlib的库
import numpy as np # 导入numpy的包
from sklearn import datasets #导入数据集
from sklearn.decomposition import PCA # PCA主成分分析类
iris = datasets.load_iris() #加载iris数据集
X = iris.data #加载特征数据
# Y = iris.target #加载标签数据
#绘制数据分布图
y = iris.target
X = iris.data
#X.shape
#调用PCA
pca = PCA(n_components=2) # 降到2维
pca = pca.fit(X) #拟合模型
X_dr = pca.transform(X) #获取新矩阵 （降维后的）
#X_dr
#也可以fit_transform一步到位
X_dr = PCA(2).fit_transform(X)
plt.figure()
plt.scatter(X_dr[y==0, 0], X_dr[y==0, 1], c="red", label=iris.target_names[0])
plt.scatter(X_dr[y==1, 0], X_dr[y==1, 1], c="black", label=iris.target_names[1])
plt.scatter(X_dr[y==2, 0], X_dr[y==2, 1], c="orange", label=iris.target_names[2])
plt.legend()
plt.title('PCA of IRIS dataset')
plt.show()
print("===K-means聚类===")
from sklearn.cluster import KMeans # 引入KMeans模块
estimator = KMeans(n_clusters=3).fit(X) # 构造聚类器
label_pred = estimator.labels_ # 获取聚类标签
#绘制k-means结果
x0 = X_dr[label_pred == 0]# 获取聚类标签等于0的话，则赋值给x0
x1 = X_dr[label_pred == 1]# 获取聚类标签等于1的话，则赋值给x1
x2 = X_dr[label_pred == 2]# 获取聚类标签等于2的话，则赋值给x2
plt.scatter(x0[:, 0], x0[:, 1], c="red", marker='o', label='label 0')#画label 0的散点图
plt.scatter(x1[:, 0], x1[:, 1], c="green", marker='*', label='label 1')#画label 1的散点图
plt.scatter(x2[:, 0], x2[:, 1], c="blue", marker='+', label='label 2')#画label 2的散点图
plt.xlabel('K-means')# 设置X轴的标签为K-means
# plt.legend(loc=2)# 设置图标在左上角
plt.title("kmeans+PCA")
plt.show()
x0 = X[label_pred == 0]# 获取聚类标签等于0的话，则赋值给x0
x1 = X[label_pred == 1]# 获取聚类标签等于1的话，则赋值给x1
x2 = X[label_pred == 2]# 获取聚类标签等于2的话，则赋值给x2
plt.scatter(x0[:, 0], x0[:, 1], c="red", marker='o', label='label 0')#画la
plt.scatter(x1[:, 0], x1[:, 1], c="green", marker='*', label='label 1')#画label 1的散点图
plt.scatter(x2[:, 0], x2[:, 1], c="blue", marker='+', label='label 2')#画label 2的散点图
plt.xlabel('K-means')# 设置X轴的标签为K-means
# plt.legend(loc=2)# 设置图标在左上角
plt.title("kmeans-features[0:2]")
plt.show()
#密度聚类之DBSCAN算法
print("===DBSCAN聚类===")
from sklearn.cluster import DBSCAN # 引入DBSCAN模块
dbscan = DBSCAN(eps=1.0, min_samples=3).fit(X) #导入DBSCAN模块进行训练,在一个邻域的半径内min_samples数的邻域eps被认为是一个簇。请记住，初始点包含在min_samples中。
label_pred = dbscan.labels_ # labels为每个数据的簇标签,不在任何"高密度"集群中的"noisy"样本返回-1
x0 = X[label_pred == 0] # 获取聚类标签等于0的话，则赋值给x0
x1 = X[label_pred == 1] # 获取聚类标签等于1的话，则赋值给x1
x2 = X[label_pred == 2] # 获取聚类标签等于2的话，则赋值给x2
plt.scatter(x0[:, 0], x0[:, 1], c="red", marker='o', label='label0') # 画label 0的散点图
plt.scatter(x1[:, 0], x1[:, 1], c="green", marker='*', label='label1') # 画label 1的散点图
plt.scatter(x2[:, 0], x2[:, 1], c="blue", marker='+', label='label2') # 画label 2的散点图
plt.xlabel('DBSCAN')# 设置X轴的标签为DBSCAN
plt.legend(loc=2)# 设置图标在左上角
plt.title("DBSCAN-features[0:2]")
plt.show()
x0 = X_dr[label_pred == 0]# 获取聚类标签等于0的话，则赋值给x0
x1 = X_dr[label_pred == 1]# 获取聚类标签等于1的话，则赋值给x1
x2 = X_dr[label_pred == 2]# 获取聚类标签等于2的话，则赋值给x2
plt.scatter(x0[:, 0], x0[:, 1], c="red", marker='o', label='label 0')#画label 0的散点图
plt.scatter(x1[:, 0], x1[:, 1], c="green", marker='*', label='label 1')#画label 1的散点图
plt.scatter(x2[:, 0], x2[:, 1], c="blue", marker='+', label='label 2')#画label 2的散点图
plt.xlabel('DBSCAN')# 设置X轴的标签为K-means
# plt.legend(loc=2)# 设置图标在左上角
plt.title("DBSCAN+PCA")
plt.show()
from sklearn_lvq import GlvqModel
import numpy as np
from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage, dendrogram
def getLinkageMat(model):
children = model.children_
cs = np.zeros(len(children))
N = len(model.labels_)
for i,child in enumerate(children):
count = 0
for idx in child:
count += 1 if idx < N else cs[idx - N]
cs[i] = count
return np.column_stack([children, model.distances_, cs])
from sklearn.cluster import AgglomerativeClustering
from sklearn.datasets import make_blobs
model = AgglomerativeClustering( n_clusters=3)
model = model.fit(X)
label_pred = model.labels_ # labels为每个数据的簇标签,不在任何"高密度"集群中的"noisy"样本返回-1
Z = linkage(X, method='ward', metric='euclidean')
p = dendrogram(Z, 0)
plt.show()
x0 = X_dr[label_pred == 0]# 获取聚类标签等于0的话，则赋值给x0
x1 = X_dr[label_pred == 1]# 获取聚类标签等于1的话，则赋值给x1
x2 = X_dr[label_pred == 2]# 获取聚类标签等于2的话，则赋值给x2
plt.scatter(x0[:, 0], x0[:, 1], c="red", marker='o', label='label 0')#画label 0的散点图
plt.scatter(x1[:, 0], x1[:, 1], c="green", marker='*', label='label 1')#画label 1的散点图
plt.scatter(x2[:, 0], x2[:, 1], c="blue", marker='+', label='label 2')#画label 2的散点图
plt.xlabel('AgglomerativeClustering')# 设置X轴的标签为K-means
# plt.legend(loc=2)# 设置图标在左上角
plt.title("AgglomerativeClustering+PCA")
plt.show()
def FCM(X, c_clusters=3, m=2, eps=10):
membership_mat = np.random.random((len(X), c_clusters)) # 生成随机二维数组shape(150,3)，随机初始化隶属矩阵
# 这一步的操作是为了使Xi的隶属度总和为1
membership_mat = np.divide(membership_mat, np.sum(membership_mat, axis=1)[:, np.newaxis])
while True:
working_membership_mat = membership_mat ** m # shape->(150,3)
# 根据公式计算聚类中心点Centroids.shape->(3,4)
Centroids = np.divide(np.dot(working_membership_mat.T, X), np.sum(working_membership_mat.T, axis=1)[:, np.newaxis])
# 该矩阵保存所有实点到每个聚类中心的欧式距离
n_c_distance_mat = np.zeros((len(X), c_clusters)) # shape->(150,3)
for i, x in enumerate(X):
for j, c in enumerate(Centroids):
n_c_distance_mat[i][j] = np.linalg.norm(x-c, 2) # 计算l2范数(欧氏距离)
new_membership_mat = np.zeros((len(X), c_clusters))
# 根据公式计算模糊矩阵U
for i, x in enumerate(X):
for j, c in enumerate(Centroids):
new_membership_mat[i][j] = 1. / np.sum((n_c_distance_mat[i][j] / n_c_distance_mat[i]) ** (2 / (m-1)))
if np.sum(abs(new_membership_mat - membership_mat)) < eps:
break
membership_mat = new_membership_mat
return np.argmax(new_membership_mat, axis=1)
fcm =FCM(X, c_clusters=3)
x0 = X_dr[fcm == 0]# 获取聚类标签等于0的话，则赋值给x0
x1 = X_dr[fcm == 1]# 获取聚类标签等于1的话，则赋值给x1
x2 = X_dr[fcm == 2]# 获取聚类标签等于2的话，则赋值给x2
plt.scatter(x0[:, 0], x0[:, 1], c="red", marker='o', label='label 0')#画label 0的散点图
plt.scatter(x1[:, 0], x1[:, 1], c="green", marker='*', label='label 1')#画label 1的散点图
plt.scatter(x2[:, 0], x2[:, 1], c="blue", marker='+', label='label 2')#画label 2的散点图
plt.xlabel('FCM')# 设置X轴的标签为K-means
# plt.legend(loc=2)# 设置图标在左上角
plt.title("FCM+PCA")
plt.show()
zX, y_true = make_blobs(n_samples=150, centers=2, cluster_std=0.6, random_state=0)
lvq = GlvqModel()
lvq.fit(X, y)
# 可视化聚类结果
lvqp = lvq.predict(X)
x0 = X_dr[lvqp == 0]# 获取聚类标签等于0的话，则赋值给x0
x1 = X_dr[lvqp == 1]# 获取聚类标签等于1的话，则赋值给x1
x2 = X_dr[lvqp == 2]# 获取聚类标签等于2的话，则赋值给x2
plt.scatter(x0[:, 0], x0[:, 1], c="red", marker='o', label='label 0')#画label 0的散点图
plt.scatter(x1[:, 0], x1[:, 1], c="green", marker='*', label='label 1')#画label 1的散点图
plt.scatter(x2[:, 0], x2[:, 1], c="blue", marker='+', label='label 2')#画label 2的散点图
plt.xlabel('lvq')# 设置X轴的标签为K-means
# plt.legend(loc=2)# 设置图标在左上角
plt.title("lvq+PCA")
plt.show()运行结果：
