DBSCAN (Density-Based Spatial Clustering of Applications with Noise) — AI Meets Finance: Algorithms Series
Introduction
Making sense of vast amounts of data is a critical task. One tool that can help in this regard is the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. This unsupervised machine learning technique helps detect clusters and anomalies in data without needing to know how many clusters exist beforehand. In this article, we will break down DBSCAN and explore its applications in finance, showing how it can be used to spot market patterns, manage risk, and uncover hidden insights in trading strategies.
What is DBSCAN?
DBSCAN is a powerful clustering algorithm designed to group data points based on their density. Unlike other clustering algorithms like K-means, which require you to define the number of clusters beforehand, DBSCAN can find clusters of arbitrary shapes and sizes by analyzing the density of the data. It’s especially useful when your data contains noise — outliers or data points that don’t belong to any meaningful cluster. In finance, where anomalies and outliers can reveal valuable insights, this feature of DBSCAN makes it incredibly valuable.
