That concludes our chapter on clustering. We learned what clustering is, where it is used, and why. We then looked at various clustering algorithms, such as k-means, hierarchical clustering, and spectral clustering. We explored how to compress images using k-means. We also learned how to evaluate clusters using various methods. Finally, we looked at how to cluster datasets using the hierarchical clustering and spectral clustering methods.
In the next chapter, we will learn about dimensionality reduction, which may be considered another form of unsupervised learning.
