聚类算法之层次聚类

分级聚类就是一棵树

加入我们有如下一张图

那么通过聚类之后形成一颗如下的树：

现在就分好了级，而且还能看出距离关系， 很明显ab之间的距离比de之间的距离要短

#coding:utf-8import osimport sysimport chardetfrom math import sqrtfrom PIL import Image, ImageDrawimport randomdef readFile(fileName):    lines = [line for line in file(fileName)]    colNames = lines[0].strip().split('\t')[1:]    rowNames = []    data = []    for line in lines[1:]:        p = line.strip().split('\t')        rowNames.append(p[0])        data.append([float(x) for x in p[1:]])    return rowNames, colNames, datadef pearsonBeta(v1, v2):    sum1 = sum(v1)    sum2 = sum(v2)        sum1Sq = sum([pow(v, 2) for v in v1])    sum2Sq = sum([pow(v, 2) for v in v2])        pSum = sum([v1[i] * v2[i] for i in range(len(v1))])        nums = pSum - (sum1 * sum2 / len(v1))    den = sqrt((sum1Sq - pow(sum1, 2) / len(v1)) * (sum2Sq - pow(sum2, 2) / len(v2)))    if(den == 0):        return 0    return 1.0 - nums/den#距离函数def pearson(v1, v2):    sum1 = sum(v1)    sum2 = sum(v2)    eSum1 = sum1 / len(v1)     eSum2 = sum2 / len(v2)        pSum = sum([(v1[i] - eSum1) * (v2[i] - eSum2) for i in range(len(v1))])    pTmp1 = sqrt(sum([pow(v1[i] -eSum1, 2) for i in range(len(v1))]))    pTmp2 = sqrt(sum([pow(v2[i] -eSum2, 2) for i in range(len(v2))]))    pSqrtSum = pTmp1 * pTmp2    if pSqrtSum == 0:        return 0        return 1 -  pSum / pSqrtSum#距离函数2def tanimoto(v1, v2):    c1, c2, shr = 0, 0, 0    for i in range(len(v1)):        if v1[i] != 0: c1 += 1        if v2[i] != 0: c2 += 1        if v1[i] != 0 and v2[i] != 0: shr += 1    return 1.0 - float(shr) / (float(c1 + c2 - shr))class bicluster:    def __init__(self, vec, left = None, right = None, distance = 0.0, id = None):        self.vec = vec        self.left = left        self.right = right        self.distance = distance        self.id = id    def vis(self):        print self.vec#层次聚类def hCluster(rows, distanceFunc = pearson):    distances = {}    currentClustId = -1    clust = [bicluster(rows[i], id = i) for i in range(len(rows))]        while len(clust) > 1:        lowestPair = (0, 1)        closest = distanceFunc(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)] = distanceFunc(clust[i].vec, clust[j].vec)                                    d = distances[(clust[i].id, clust[j].id)]  #直接写成了i，j ， 害我找了半天                if d < closest:                    closest = d                    lowestPair = (i, j)               mergevec = [(clust[lowestPair[0]].vec[i] + clust[lowestPair[1]].vec[i]) / 2.0 for i in range(len(clust[0].vec))]        newCluster = bicluster(mergevec, left = clust[lowestPair[0]], right = clust[lowestPair[1]], distance = closest, id = currentClustId)                currentClustId -= 1        del clust[lowestPair[1]]   #must first del 1, then 0        del clust[lowestPair[0]]        clust.append(newCluster)    return clust[0]#k-均值聚类def kcluster(rows, distanceFunc = pearson, k = 5):    ranges = [(min(row[i] for row in rows), max(row[i] for row in rows)) for i in range(len(rows[0]))]        clusters = [[random.random() * (ranges[i][1] - ranges[i][0]) + ranges[i][0] for i in range(len(rows[0]))] for j in range(k)]        bestMatches = None    for t in range(100):        print "iter is: %d" %(t)        lastMatches = [[] for i in range(k)]        for i in range(len(rows)):            row = rows[i]            lastMatch = 0            for j in range(k):                d = distanceFunc(clusters[j], row)                if d < distanceFunc(rows[lastMatch], row):                    lastMatch = j            lastMatches[lastMatch].append(i)                            if lastMatches == bestMatches:            break;        bestMatches = lastMatches                #move center        for i in range(k):            if len(bestMatches[i]) > 0:                newRow = []                for j in range(len(rows[0])):                    sum = 0                    for v in range(len(bestMatches[i])):                        sum += rows[v][j]                    newRow.append(sum)                for j in range(len(newRow)):                    newRow[j] = newRow[j] / len(bestMatches[i])                clusters[i] = newRow                       return bestMatches            #以缩进方式打印层次聚类的树def printClust(clust, labels = None, n = 0):    for i in range(n):print ' ',    if clust.id < 0:        print '-'    else:        if labels == None:            print clust.id        else:            print labels[clust.id]    if clust.left != None:        printClust(clust.left, labels = labels, n = n + 1)    if clust.right != None:        printClust(clust.right, labels = labels, n = n + 1)        def getHeight(clust):    if clust.left == None and clust.right == None:        return 1    return getHeight(clust.left) + getHeight(clust.right)def getDepth(clust):    if clust.left == None and clust.right == None:        return 1    return max(getDepth(clust.left), getDepth(clust.right)) + clust.distancedef drawnode(draw, clust, x, y, scaling, labels):    if clust.id < 0:        h1 = getHeight(clust.left) * 20        h2 = getHeight(clust.right) * 20        top = y - (h1 + h2) / 2        bottom = y + (h1 + h2) / 2                li = clust.distance * scaling        draw.line((x, top + h1/2, x, bottom - h2/2), fill = (255, 0, 0))                draw.line((x, top + h1/2, x + li, top + h1/2), fill = (255, 0, 0))        draw.line((x ,bottom - h2/2, x + li, bottom - h2/2), fill = (255, 0, 0))                drawnode(draw, clust.left, x + li, top + h1/2, scaling, labels)        drawnode(draw, clust.right, x + li, bottom - h2/2, scaling, labels)    else:        draw.text((x + 5, y - 7), labels[clust.id], (0, 0, 0))    #以属性结构打印层次聚类的关系def drawdendrogram(clust, labels, jpeg = "zebo2.jpg"):    h = getHeight(clust) * 20    w = 1200    depth = getDepth(clust)        scaling = float(w - 150) / depth        img = Image.new("RGB", (w, h), (255, 255, 255))    draw = ImageDraw.Draw(img)        draw.line((0, h/2, 10, h/2), fill = (255, 0, 0))        drawnode(draw, clust, 10, (h/2), scaling, labels)    img.save(jpeg, "JPEG")  (wants, people, data) = readFile("F:\\py\\dataFetch\\julei\\data\\blogdata.txt")clust = hCluster(data, distanceFunc = pearson)printClust(clust, wants)#drawdendrogram(clust, wants)#print kcluster(data)#cluster = hCluster(data, distanceFunc = tanimoto)#drawdendrogram(cluster, rowNames)

那么执行这个程序之后生成如下一张图片：

本程序所需的数据是某某博客出现某某关键字的次数的一个矩阵， 不过现在官网的链接打不开了

附如下链接：http://pan.baidu.com/s/17SqJS

第一列是博客名， 后面的每一列都是单词在改博客出现的次数