23. Hierarchical Clustering¶
An implementation of hierarchical clustering is provided in the SciPy package. Among other things, it allows to build clusters from similarity matrices and make dendrogram plots. The following example shows how to do this for an already computed similarity matrix. The similarity data are computed from events in an area with induced seismicity (using the cross-correlation routines in obspy.signal) and can be fetched from our examples webserver:
First, we import the necessary modules and load the data stored on our webserver:
>>> import io, urllib
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy.cluster import hierarchy
>>> from scipy.spatial import distance
>>>
>>> url = "https://examples.obspy.org/dissimilarities.npz"
>>> with io.BytesIO(urllib.urlopen(url).read()) as fh, np.load(fh) as data:
... dissimilarity = data['dissimilarity']
Now, we can start building up the plots. First, we plot the dissimilarity matrix:
>>> plt.subplot(121)
>>> plt.imshow(1 - dissimilarity, interpolation="nearest")
After that, we use SciPy to build up and plot the dendrogram into the right-hand subplot:
>>> dissimilarity = distance.squareform(dissimilarity)
>>> threshold = 0.3
>>> linkage = hierarchy.linkage(dissimilarity, method="single")
>>> clusters = hierarchy.fcluster(linkage, threshold, criterion="distance")
>>>
>>> plt.subplot(122)
>>> hierarchy.dendrogram(linkage, color_threshold=0.3)
>>> plt.xlabel("Event number")
>>> plt.ylabel("Dissimilarity")
>>> plt.show()
(Source code, png, hires.png)
