将SciPy层次树形图切割成多个阈值的聚类

Question

我想将SciPy的树形图切割成多个阈值上的多个簇。

我尝试过使用fcluster但它只能在一个阈值上切割。

（以下是我从其他问题中提取的一段代码。）

import pandas

data = pandas.DataFrame({
'total_runs': {0: 2.489857755536053, 1: 1.2877651950650333, 2: 0.8898850111727028, 3: 0.77750321282732704, 4: 0.72593099987615461, 5: 0.70064977003207007, 6:0.68217502514600825,7: 0.67963194285399975, 8: 0.64238326692987524, 9:0.6102581538587678, 10: 0.52588765899448564, 11: 0.44813665774322564, 12: 0.30434031343774476, 13: 0.26151929543260161, 14: 0.18623657993534984, 15: 0.17494230269731209,16: 0.14023670906519603, 17: 0.096817318756050832, 18:0.085822227670014059, 19: 0.042178447746868117, 20: -0.073494398270518693,21: -0.13699665903273103, 22: -0.13733324345373216, 23: -0.31112299949731331, 24: -0.42369178918768974, 25: -0.54826542322710636,26: -0.56090603814914863, 27: -0.63252372328438811, 28: -0.68787316140457322,29: -1.1981351436422796, 30: -1.944118415387774,31: -2.1899746357945964, 32: -2.9077222144449961}, 
'total_salaries': {0: 3.5998991340231234,1: 1.6158435140488829, 2: 0.87501176080187315, 3: 0.57584734201367749, 4: 0.54559862861592978, 5: 0.85178295446270169,6: 0.18345463930386757, 7: 0.81380836410678736, 8: 0.43412670908952178, 9: 0.29560433676606418, 10: 1.0636736398252848, 11: 0.08930130612600648, 12: -0.20839133305170349, 13: 0.33676911316165403, 14: -0.12404710480916628, 15: 0.82454221267393346,16: -0.34510456295395986, 17: -0.17162157282367937, 18: -0.064803261585569982, 19: -0.22807757277294818, 20: -0.61709008778669083,21: -0.42506873158089231, 22: -0.42637946918743924, 23: -0.53516500398181921, 24: -0.68219830809296633, 25: -1.0051418692474947,26: -1.0900316082184143, 27: -0.82421065378673986, 28: 0.095758053930450004, 29: -0.91540963929213015, 30: -1.3296449323844519,31: -1.5512503530547552, 32: -1.6573856443389405}
})


from scipy.spatial.distance import pdist
from scipy.cluster.hierarchy import linkage, dendrogram

distanceMatrix = pdist(data)
dend = dendrogram(linkage(distanceMatrix, method='complete'), 
       color_threshold=4, 
       leaf_font_size=10,
       labels = df.teamID.tolist())

Dendrogram

因此，对于上面的树形图，我想对Green集群进行3分割，但对于Blue＆amp;红色群集切割应该在5（因此它们都在一个群集中）。

Answer 1

方法fcluster可以使用monocrit参数执行此操作，这样您就可以精确查明树形图上的切割位置。你想在-1和-3的位置进行切割，其中-1是树的顶部，-3是从上到下计数的第三个节点（蓝色与绿色相交）。这是如何：

Z = linkage(distanceMatrix, method='complete')
monocrit = np.zeros((Z.shape[0], ))
monocrit[[-1, -3]] = 1
fc = fcluster(Z, 0, criterion='monocrit', monocrit=monocrit)

通过仅在严格大于阈值（即0）的值的节点处执行分离来形成平坦簇。

为了说明这一点，我首先使用带编号的叶子重做树形图：

dend = dendrogram(Z, color_threshold=4, leaf_font_size=10, labels = range(33))

然后打印扁平簇：

for k in range(1, 4):
    print(np.where(fc == k))

他们是

(array([30, 31, 32]),)
(array([12, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]),)
(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 13, 15]),)

因此，绿色分为两部分，红色和蓝色分开。