时间:2020-10-03 10:13:50 | 栏目:Python代码 | 点击:次
本文实例讲述了Python聚类算法之凝聚层次聚类。分享给大家供大家参考,具体如下:
凝聚层次聚类:所谓凝聚的,指的是该算法初始时,将每个点作为一个簇,每一步合并两个最接近的簇。另外即使到最后,对于噪音点或是离群点也往往还是各占一簇的,除非过度合并。对于这里的“最接近”,有下面三种定义。我在实现是使用了MIN,该方法在合并时,只要依次取当前最近的点对,如果这个点对当前不在一个簇中,将所在的两个簇合并就行:
单链(MIN):定义簇的邻近度为不同两个簇的两个最近的点之间的距离。
全链(MAX):定义簇的邻近度为不同两个簇的两个最远的点之间的距离。
组平均:定义簇的邻近度为取自两个不同簇的所有点对邻近度的平均值。
# scoding=utf-8 # Agglomerative Hierarchical Clustering(AHC) import pylab as pl from operator import itemgetter from collections import OrderedDict,Counter points = [[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 = 3 finalGroup = 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()
运行效果截图如下:
希望本文所述对大家Python程序设计有所帮助。