In this chapter, we discussed our first unsupervised learning task: clustering. Clustering is used to discover structure in unlabeled data. You learned about the K-Means clustering algorithm, which iteratively assigns instances to clusters and refines the positions of the cluster centroids. While K-Means learns from experience without supervision, its performance is still measurable; you learned to use distortion and the silhouette coefficient to evaluate clusters. We applied K-Means to two different problems. First, we used K-Means for image quantization, a compression technique that represents a range of colors with a single color. We also used K-Means to learn features in a semi-supervised image classification problem.

In the next chapter, we will discuss another unsupervised learning task called dimensionality reduction. Like the semi-supervised feature representations we created to classify images of cats and dogs, dimensionality reduction can be used to reduce the dimensions of a set of explanatory variables while retaining as much information as possible.