In [63]:
%matplotlib inline
from matplotlib import pyplot as plt
from sklearn import datasets
import numpy as np
import pandas as pd
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.cluster.hierarchy import cophenet
from scipy.spatial.distance import pdist
In [64]:
data, labels_true = datasets.make_blobs(n_samples=750, centers=[[1,1],[0,5],[2,8]], cluster_std=0.7,
random_state=0)
plt.scatter(data[:,0], data[:,1])
df = pd.DataFrame(data, columns=['X', 'Y'])
In [65]:
Z = linkage(df, 'ward')
c, coph_dists = cophenet(Z, pdist(df, metric='euclidean'))
c
Out[65]:
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