2#cluster.ppt - DBSCAN u2013 Density-Based Spatial...

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DBSCAN – Density-Based Spatial Clustering of Applications with Noise M.Ester, H.P.Kriegel, J.Sander and Xu. A density-based algorithm for discovering clusters in large spatial databases, Aug 1996 Reference:
DBSCAN Density-based Clustering locates regions of high density that are separated from one another by regions of low density. Density = number of points within a specified radius (Eps) DBSCAN is a density-based algorithm. A point is a core point if it has more than a specified number of points (MinPts) within Eps These are points that are at the interior of a cluster A border point has fewer than MinPts within Eps, but is in the neighborhood of a core point
DBSCAN A noise point is any point that is not a core point or a border point. Any two core points are close enough– within a distance Eps of one another – are put in the same cluster Any border point that is close enough to a core point is put in the same cluster as the core point Noise points are discarded
Border & Core Core Border Outlier = 1unit MinPts = 5
Concepts: ε-Neighborhood ε-Neighborhood ε-Neighborhood - Objects within a radius of ε from an object. (epsilon-neighborhood)

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