How does DBSCAN handle outliers and irregular-shaped clusters?

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AcadiFi · Accepted Answer

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) groups points that are densely packed together while labeling sparse points as noise. It requires two parameters: epsilon, the neighborhood radius, and minPts, the minimum points to qualify as a dense region. A point is a core point if at least minPts neighbors lie within epsilon, a border point if it lies within epsilon of a core point but is not itself dense, and noise otherwise.

For a set of 350 hedge fund returns at a research shop like Orinford Analytics, compute pairwise distances on standardized factor loadings. With epsilon 0.6 and minPts 5, DBSCAN might find 6 clusters of correlated strategies plus 42 noise points representing idiosyncratic or distressed funds that do not fit style boxes. K-means would force these 42 funds into clusters, distorting centroids.

DBSCAN handles non-convex shapes because it follows density rather than distance from a centroid. It does not require K in advance. The weakness is parameter sensitivity: epsilon must match the typical scale of cluster density, and high-dimensional data suffers from distance concentration. Use the k-distance plot (sort distances to the k-th nearest neighbor and find the elbow) to choose epsilon.

For CFA candidates, DBSCAN is particularly useful for anomaly-aware clustering, detecting both styles and outlier regimes in one pass. Pair it with PCA to reduce dimensions before applying DBSCAN when features exceed ten.

How does DBSCAN handle outliers and irregular-shaped clusters?

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