初识聚类算法: 凝聚层次聚类，初识聚类,[Python]代码#

初识聚类算法: 凝聚层次聚类，初识聚类,[Python]代码#

[Python]代码

# scoding=utf-8# Agglomerative Hierarchical Clustering(AHC)import pylab as plfrom operator import itemgetterfrom collections import OrderedDict,Counterpoints = [[int(eachpoint.split('#')[0]), int(eachpoint.split('#')[1])] for eachpoint in open("points","r")]# 初始时每个点指派为单独一簇groups = [idx for idx in range(len(points))]# 计算每个点对之间的距离disP2P = {}for idx1,point1 in enumerate(points):    for idx2,point2 in enumerate(points):        if (idx1 < idx2):            distance = pow(abs(point1[0]-point2[0]),2) + pow(abs(point1[1]-point2[1]),2)            disP2P[str(idx1)+"#"+str(idx2)] = distance# 按距离降序将各个点对排序disP2P = OrderedDict(sorted(disP2P.iteritems(), key=itemgetter(1), reverse=True))# 当前有的簇个数groupNum = len(groups)# 过分合并会带入噪音点的影响，当簇数减为finalGroupNum时，停止合并finalGroupNum = int(groupNum*0.1)while groupNum > finalGroupNum:    # 选取下一个距离最近的点对    twopoins,distance = disP2P.popitem()    pointA = int(twopoins.split('#')[0])    pointB = int(twopoins.split('#')[1])    pointAGroup = groups[pointA]    pointBGroup = groups[pointB]    # 当前距离最近两点若不在同一簇中，将点B所在的簇中的所有点合并到点A所在的簇中，此时当前簇数减1    if(pointAGroup != pointBGroup):        for idx in range(len(groups)):            if groups[idx] == pointBGroup:                groups[idx] = pointAGroup        groupNum -= 1# 选取规模最大的3个簇，其他簇归为噪音点wantGroupNum = 3finalGroup = Counter(groups).most_common(wantGroupNum)finalGroup = [onecount[0] for onecount in finalGroup]dropPoints = [points[idx] for idx in range(len(points)) if groups[idx] not in finalGroup]# 打印规模最大的3个簇中的点group1 = [points[idx] for idx in xrange(len(points)) if groups[idx]==finalGroup[0]]group2 = [points[idx] for idx in xrange(len(points)) if groups[idx]==finalGroup[1]]group3 = [points[idx] for idx in xrange(len(points)) if groups[idx]==finalGroup[2]]pl.plot([eachpoint[0] for eachpoint in group1], [eachpoint[1] for eachpoint in group1], 'or')pl.plot([eachpoint[0] for eachpoint in group2], [eachpoint[1] for eachpoint in group2], 'oy')pl.plot([eachpoint[0] for eachpoint in group3], [eachpoint[1] for eachpoint in group3], 'og')   # 打印噪音点，黑色pl.plot([eachpoint[0] for eachpoint in dropPoints], [eachpoint[1] for eachpoint in dropPoints], 'ok')   pl.show()

HAC.png

imgs/asCode/22213425_4IlT.png