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 skill set:

1. We start by generating 10 random data points, just like in the previous screenshot:

In [1]: from sklearn.datasets import make_blobs
...     X, y = make_blobs(random_state=100, n_samples=10)

2. 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)

3. Fitting the model to the data works, as usual, via the fit_predict method:

In [3]: labels = agg.fit_predict(X)

4. 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.scatter(X[:, 0], X[:, 1], c=labels, s=100)

The resulting clustering is equivalent to the following diagram:

Finally, before we end this chapter, let's look at how to compare clustering algorithms and choose the correct clustering algorithm for the data you have!