scikit-learn : Machine Learning Simplified

Chapter 6. Clustering with K-Means

In the previous chapters we discussed supervised learning tasks; we examined algorithms for regression and classification that learned from labeled training data. In this chapter we will discuss an unsupervised learning task called clustering. Clustering is used to find groups of similar observations within a set of unlabeled data. We will discuss the K-Means clustering algorithm, apply it to an image compression problem, and learn to measure its performance. Finally, we will work through a semi-supervised learning problem that combines clustering with classification.

Recall from Chapter 1, The Fundamentals of Machine Learning, that the goal of unsupervised learning is to discover hidden structure or patterns ...