Clustering with Machine Learning
From the last few chapters, we know how to use supervised learning, including using and comparing different models, optimizing hyperparameters, and evaluating models. One of the other major categories of machine learning is clustering. Unlike supervised learning, clustering and unsupervised learning do not require targets or labels for the data. We can still use clustering with labeled data, however, but all inputs are treated as features. Clustering uncovers patterns in data based on the similarity of data points. There are several different methods for performing clustering, but they all rely on distances between data points. In this chapter, we'll learn how to use some of the key clustering methods and will learn how they work, including:
- k-means clustering
- DBSCAN
- Hierarchical clustering
Near the end of the chapter, some of the other models available will be discussed as well as how we can use clustering as part...
