# DBSCAN > _DBSCAN_ = Density-Based Spatial Clustering of Applications of Noise ## Algorithm - Find core data points of high density and expand clusters from them by neighborhoods - Parameters - $\epsilon$ = max radius of neighborhood - _Minimum Points_ (_MinPts_) = min num of points in the neighborhood - $N_\epsilon(q)$ = the neighborhood - Points - Core points - with valid neighborhood - Border points - Noise/Outlier An [animation](https://www.naftaliharris.com/blog/visualizing-dbscan-clustering/). ## Pros and Cons - Pros - Resistant to noise/outliers - Arbitrary cluster shape - Efficiency: one scan - Cons - Sensitive to parameters chosen