模式识别层次聚类

聚类顺序为(x2,x4),(x2,x3,x4),(x2,x3,x4,x5),(x1,x2,x3,x4,x5),样本距离计算选择single linkage ，和图聚类顺序一致。

import numpy as np
from scipy.cluster.hierarchy import dendrogram,linkage
from matplotlib import pyplot as plt
import math
x1=[0,1,3,1,3,4]
x2=[0,0,1,0,1,0]
x3=[2,1,0,2,2,1]
x4=[1,0,0,0,1,1]
x5=[3,3,3,1,2,1]
data=[x1,x2,x3,x4,x5]

data=np.array(data)
reslut=linkage(data,method='single')
dendrogram(reslut)
plt.show()

def juli(a: list, b: list)  -> int:
    al=len(a)
    bl=len(b)
    jl=0
    for i in range(al):
        jl+=math.pow(abs(a[i]-b[i]),2)
    return jl

for i in range(len(data)):
    for j in range(len(data)):
        print("x%d-x%d的距离为%d"%(i+1,j+1,juli(data[i],data[j])))