Welcome to the fourth section of the course! In this section, we will learn the last (but not the last overall) clustering method.

This method is heavily based on math, such as eigenvalues, laplacians, graphs, and kernels. We will not dig into the algorithm itself but will consider the main cases of its usage.

You might forget all the charts you saw in the course, but in the second section, we considered an interesting set of points. Look at the scatterplot below.

And look below at the results of the K-Means algorithm.

Well, that's not what we were looking for. So, how can you implement the spectral clustering algorithm in Python? The key function is SpectralClustering from sklearn.cluster. The algorithm of prediction is the same as in the previous section:

1. Create SpectralClustering model with k clusters using n_clusters = k parameter.

2. Fit the numerical data and predict the labels using the .fit_predict() method of model and passing numerical data as a parameter.

Then, if needed, you can visualize the results.

Let's find out can the algorithm heavily based on strong math correctly define the clusters?