层次聚类

import math
import pylab as pl
#数据集
data = """
1,0.697,0.46,2,0.774,0.376,3,0.634,0.264,4,0.608,0.318,5,0.556,0.215,
6,0.403,0.237,7,0.481,0.149,8,0.437,0.211,6,0.666,0.061,10,0.243,0.267,
11,0.245,0.057,12,0.343,0.066,13,0.636,0.161,14,0.657,0.168,15,0.36,0.37,
16,0.593,0.042,17,0.716,0.103,18,0.356,0.188,16,0.336,0.241,20,0.282,0.257,
21,0.748,0.232,22,0.714,0.346,23,0.483,0.312,24,0.478,0.437,25,0.525,0.366,
26,0.751,0.486,27,0.532,0.472,28,0.473,0.376,29,0.725,0.445,30,0.446,0.456"""
#数据处理 
a=data.split(',')
dataset=[(float(a[i]),float(a[i+1])) for i in range(1,len(a)-1,3)]
#计算欧几里得距离,a,b分别为两个元组
def dist(a,b):
    return math.sqrt(math.pow(a[0]-b[0],2)+math.pow(a[1]-b[1],2))
def dist_min(Ci,Cj):
    return min(dist(i,j) for i in Ci for j in Cj)
def dist_max(Ci,Cj):
    return max(dist(i,j) for i in Ci for j in Cj)
def dist_avg(Ci,Cj):
    return sum(dist(i,j) for i in Ci for j in Cj)/(len(Ci)*len(Cj))
#距离最小的下标
def find_Min(M):
    min=1000
    x=0;y=0
    for i in range(len(M)):
        for j in range(len(M[i])):
            if i!=j and M[i][j]<min:
                min=M[i][j];x=i; y=j
    return (x,y,min)
def AGNES(dataset, dist, k):
    #初始化C和M
    C=[];M=[]
    for i in dataset:
        Ci=[]
        Ci.append(i)
        C.append(Ci)
    for i in C:
        Mi=[]
        for j in C:
            Mi.append(dist(i,j))
        M.append(Mi)
    q=len(dataset)
    #合并更新
    while q>k:
        x,y,min=find_Min(M)
        C[x].extend(C[y])
        C.remove(C[y])
        M=[]
        for i in C:
            Mi=[]
            for j in C:
                Mi.append(dist(i,j))
            M.append(Mi)
        q-=1
    return C
def draw(C):
    colValue=['r','y','g','b','c','k','m']
    for i in range(len(C)):
        coo_X=[]
        coo_Y=[]
        for j in range(len(C[i])):
            coo_X.append(C[i][j][0])
            coo_Y.append(C[i][j][1])
        pl.scatter(coo_X,coo_Y,marker='x',color=colValue[i%len(colValue)],label=i)
    pl.legend(loc='upper right')
    pl.show()
C=AGNES(dataset,dist_avg,3)
draw(C)