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DBSCAN Density-Based Spatial Clustering of Applications with NoiseM.Ester, H.P.Kriegel, J.Sander and Xu. A density-based algorithm for discovering clusters in large spatial databases, Aug 1996Reference:
Density-Based ClusteringWhy Density-Based Clustering?Results of a k-medoid algorithm for k=4Basic Idea:Clusters are dense regions in the data space, separated by regions of lower object densityDifferent density-based approaches exist Here we discuss the ideas underlying the DBSCAN algorithm
DBSCANDensity-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)
DBSCANDBSCAN is a density-based algorithm.Density = number of points within a specified radius (Eps)A point is a core pointif it has more than a specified number of points (MinPts) within EpsThese are points that are at the interior of a clusterA border pointhas fewer than MinPts within Eps, but is in the neighborhood of a core pointA noise pointis any point that is not a core point or a border point.

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