DBSCAN, which stands for Density-Based Spatial Clustering of Applications with Noise, is great at dealing with noisy data. Other methods like K-Means and hierarchical clustering can have a tough time with this. Here’s how DBSCAN works:

• Density-Based Method: Unlike K-Means, which uses a central point and can get confused by outliers, DBSCAN looks at how many points are close together. It finds groups of points that are packed together and labels points that are spread out as noise.

• Adjustable Parameters: DBSCAN uses two main settings: $\epsilon$ (this is the biggest distance to find neighbors) and $minPts$ (this is how many points you need to form a group). You can change these settings to control how it treats noise.

• No Set Number of Clusters Needed: With DBSCAN, you don’t have to decide how many clusters to look for in advance. This makes it very flexible, especially for data that comes in different shapes and sizes, while still managing noise well.

In summary, if your data has a lot of noise, DBSCAN could be the best choice for you.