dendrogram_test.py

# -*- coding: utf-8 -*-
import numpy

data = []
x = numpy.linspace(0.0, 10.0, 100)
data.extend([numpy.sin(x) + numpy.random.uniform(-0.5, 0.5, 100) for _ in range(5)])
data.extend([numpy.cos(x) + numpy.random.uniform(-0.5, 0.5, 100) for _ in range(5)])
numpy.random.shuffle(data)

import matplotlib.pylab as pylab

plt.matshow(numpy.asarray(data), aspect='auto', origin='lower', cmap='hot')
plt.show()

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

dist = squareform(pdist(data, metric='euclidean'))
dend = dendrogram(linkage(dist, method='complete'))

http://nbviewer.ipython.org/github/herrfz/dataanalysis/blob/master/week3/hierarchical_clustering.ipynb