Finding clusters: DBSCAN or Friends-of-Friends?

Question

I have searched for dark matter halos in cosmological simulations using Friends-of-Friends algorithm.

https://swift.dur.ac.uk/docs/FriendsOfFriends/algorithm_description.html

Now I search HI clouds in radio telescope data using DBSCAN algorithm.

https://en.wikipedia.org/wiki/DBSCAN

I found that these 2 algorithms have some identicals like:

• Density-Based Clustering: Both DBSCAN and FoF can be considered density-based methods in a broad sense. DBSCAN explicitly defines clusters based on the density of data points, while FoF can also identify clusters based on the density of connections (i.e., friends) between points.
• Cluster Identification: Both algorithms identify clusters by grouping together points that are close to each other in some sense. In DBSCAN, this is based on the density of points within a specified radius, while in FoF, it’s based on the connectivity or number of shared neighbors. No Need for a Predefined Number of Clusters: Neither algorithm requires the number of clusters to be specified in advance. DBSCAN determines clusters based on density parameters, and FoF finds clusters based on the connectivity structure without requiring the number of clusters to be set beforehand.
• Handling Noise and Outliers: Both methods can handle noise or outliers, though they do so differently. DBSCAN explicitly classifies points that don't fit into any cluster as noise. In FoF, points that are not sufficiently connected to other points may end up isolated or not part of any significant cluster.
• Cluster Shape Flexibility: Both algorithms are capable of identifying clusters of arbitrary shapes. DBSCAN is well-known for its ability to find clusters of various shapes and sizes due to its density-based approach. FoF can also find clusters with irregular shapes based on the connection patterns.

I want to know if there is some major difference between them so that I can better choose which to use when clustering things.