Hands-On Machine Learning for Algorithmic Trading

Density-based clustering

Density-based clustering algorithms assign cluster membership based on proximity to other cluster members. They pursue the goal of identifying dense regions of arbitrary shapes and sizes. They do not require the specification of a certain number of clusters but instead rely on parameters that define the size of a neighborhood and a density threshold (see the density_based_clustering notebook for relevant code samples).