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Although OpenCV does not provide an implementation of agglomerative hierarchical clustering, it is a popular algorithm that should, by all means, belong to our machine learning repertoire.
We start out by generating 10 random data points, just like in the previous figure:
In [1]:
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=10, random_state=100)
Using the familiar statistical modeling API, we import the AgglomerativeClustering
algorithm and specify the desired number of clusters:
In [2]:
from sklearn import cluster
agg = cluster.AgglomerativeClustering(n_clusters=3)
Fitting the model to the data works, as usual, via the fit_predict method:
In [3]:
labels = agg.fit_predict(X)
We can generate a scatter plot where every data point is colored according to the predicted label:
In [4]:
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
plt.figure(figsize=(10, 6))
plt.scatter(X[:, 0], X[:, 1], c=labels, s=100)
Out[4]:
That's it! This marks the end of another wonderful adventure.