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It's a density-based clustering algorithm

Density associated with a point is obtained by counting the number of points in a region of specified radius $\epsilon$ around each point

- points with density $\geqslant \text{min_pts}$ are considered as "core points"
- noise and non-core points are discarded
- clusters are formed around the core points
- if two core points are within a radius $\epsilon$, then they belong to the same cluster

Disadvantages

- can find clusters of different shapes, but can't find clusters of different densities

- an extension of DBSCAN that words better for high-dimensional data
- also can find clusters of different density

- Ester, Martin, et al. "A density-based algorithm for discovering clusters in large spatial databases with noise." 1996. [1]

- Ertöz, Levent, Michael Steinbach, and Vipin Kumar. "Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data." 2003. [2]