8.4 聚类(Clustering) hierarchical clustering 层次聚类应用

1. python实现：

from numpy import *class cluster_node:    def __init__(self,vec,left=None,right=None,distance=0.0,id=None):        self.left=left        self.right=right        self.vec=vec        self.id=id        self.distance=distancedef L2dist(v1,v2):    return sqrt(sum(v1-v2)**2)def L1dist(v1,v2):    return sum(abs(v1-v2))def hcluster(features,distance=L2dist):    distances={}    currentClusterId=-1    clust=[cluster_node(array(features),id=i) for i in range(len(features))]    while(len(clust)>1):        lowesPair=(0,1)        closest=distance(clust[0].vec,clust[1].vec)        for i in range(len(clust)):            for j in range(i+1,len(clust)):                if((clust[i].id,clust[j].id) not in distances):                    distances[(clust[i].id,clust[j].id)]=distance(clust[i].vec,clust[j].vec)                    d=distances[(clust[i].id,clust[j].id)]                    if d<closest:                        closest=d                        lowesPair=(i,j)        mergevec=(clust[lowesPair[0]].vec-clust[lowesPair[1].vec])/2.0                      newcluster=cluster_node(array(mergevec),left=clust[lowesPair[0]],right=clust[lowesPair[1]],distance=closest,id=currentClusterId)        currentClusterId-=1        del clust[lowesPair[0]]        del clust[lowesPair[1]]        clust.append(newcluster)    return clust[0]def extract_cluster(clust,dist):    cluster={}    if clust.distance<dist:        return [clust]    else:        cl=[]        cr=[]        if clust.left!=None:            cl=extract_cluster(clust.left, dist)        if clust.right!=None:            cr=extract_cluster(clust.right, dist)        return cl+cr