clustering_hclust.md

Note: This is a generated markdown export from the Jupyter notebook file clustering_hclust.ipynb. You can also view the notebook with the nbviewer from Jupyter.

Hierarchical Clustering (SciPy)

%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

max_samples = 20
labels = range(1, max_samples+1)

data, labels_true = datasets.make_blobs(n_samples=max_samples, centers=[[1,1],[0,5],[2,8]], cluster_std=0.7,
                            random_state=0)

df = pd.DataFrame(data, columns=['X', 'Y'])

plt.scatter(df.X, df.Y)


for label, x, y in zip(labels, df.X, df.Y):
    plt.annotate(
        label,
        xy=(x, y), xytext=(-3, 3),
        textcoords='offset points', ha='right', va='bottom')

plt.show()

linked = linkage(df, method='single', metric='euclidean')
dendrogram(linked, labels=labels, orientation='top', distance_sort='descending', show_leaf_counts=True)

plt.show()

linked = linkage(df, method='ward', metric='euclidean')
dendrogram(linked, labels=labels, orientation='top', distance_sort='descending', show_leaf_counts=True)

plt.show()

linked = linkage(df, method='complete', metric='euclidean')
dendrogram(linked, labels=labels, orientation='top', distance_sort='descending', show_leaf_counts=True)

plt.show()

linked = linkage(df, method='average', metric='euclidean')
dendrogram(linked, labels=labels, orientation='top', distance_sort='descending', show_leaf_counts=True)

plt.show()

linked = linkage(df, method='weighted', metric='euclidean')
dendrogram(linked, labels=labels, orientation='top', distance_sort='descending', show_leaf_counts=True)

plt.show()

linked = linkage(df, method='centroid', metric='euclidean')
dendrogram(linked, labels=labels, orientation='top', distance_sort='descending', show_leaf_counts=True)

plt.show()