Question

我尝试使用SciPy的dendrogram方法根据阈值将数据切割为多个群集。但是，一旦我创建了树形图并检索其color_list，列表中的条目就会少于标签。

或者，我尝试使用fcluster中使用dendrogram中标识的相同阈值的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());但是，这不会产生相同的结果 - 它给了我一个集群而不是三个集群。

这是我的代码。

len(dend['color_list'])
Out[169]: 32
len(df.index)
Out[170]: 33

dendrogram

dendrogram

为什么fcluster只为32个标签指定颜色，尽管数据中有33个观察值？这是我如何提取标签及其相应的簇（上面用蓝色，绿色和红色着色）？如果没有，我还有什么其他方法可以减少＆＃39;树好吗？

我尝试使用dend。当from scipy.cluster.hierarchy import fcluster fcluster(linkage(distanceMatrix, method='complete'), 4) Out[175]: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)的相同阈值返回三时，为什么它只为集合返回一个集群？

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Answer 1

这里是答案 - 我没有添加“距离”。作为fcluster的选项。有了它，我得到了正确的（3）群集分配。

assignments = fcluster(linkage(distanceMatrix, method='complete'),4,'distance')

print assignments
       [3 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]

cluster_output = pandas.DataFrame({'team':df.teamID.tolist() , 'cluster':assignments})

print cluster_output
    cluster team
0         3  NYA
1         2  BOS
2         2  PHI
3         2  CHA
4         2  SFN
5         2  LAN
6         2  TEX
7         2  ATL
8         2  SLN
9         2  SEA
10        2  NYN
11        2  HOU
12        1  BAL
13        2  DET
14        1  ARI
15        2  CHN
16        1  CLE
17        1  CIN
18        1  TOR
19        1  COL
20        1  OAK
21        1  MIL
22        1  MIN
23        1  SDN
24        1  KCA
25        1  TBA
26        1  FLO
27        1  PIT
28        1  LAA
29        1  WAS
30        1  ANA
31        1  MON
32        1  MIA

通过阈值将SciPy层次树状图切割成簇

1 个答案: