DBSCAN_15_3_18 (1).ppt - DBSCAN u2013 Density-Based...

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DBSCAN – Density-Based Spatial Clustering of Applications with Noise
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) In Density-based Clustering methods, clusters are grown by starting with some data points and then including the neighboring points as long as the neighborhood is sufficiently dense These methods can find clusters of arbitrary shape
DBSCAN Clusters of arbitrary shape
DBSCAN 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. A border can fall within the neighborhood of several core points
DBSCAN A noise point is any point that is neither a core point nor 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

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