Machine Learning for OpenCV

First caveat: No guarantee of finding the global optimum

Although mathematicians have proved that the expectation-maximization step improves the result in each step, there is still no guarantee that, in the end, we will find the global best solution. For example, if we use a different random seed in our simple example (such as using, seed 10 instead of 5), we suddenly get very poor results:

In [9]: centers, labels = find_clusters(X, 4, rseed=10)...     plt.scatter(X[:, 0], X[:, 1], c=labels, s=100, cmap='viridis');

This will generate the following plot:

Example of k-means missing the global optimum

What happened?

The short answer is that the ...