DensityCluster-L17

DensityCluster-L17 - CSE572/CBS572:DataMining Lecture 17:...

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1 CSE 572/CBS 572: Data Mining Lecture 17: Density-based Clustering Read Section 8.4
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2 Density-based Clustering Locates regions of high density that are separated from one another by regions of low density 6 density-based clusters High density regions Low density background
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3 Density Density-based clustering require a notion of density Examples: Euclidean density Euclidean density = number of points per unit volume Probability density Intuitively, if a probability distribution has density ƒ , then the infinitesimal interval [ x , x + dx ] has probability ƒ ( x ) dx .
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4 Euclidean Density – Cell-based Simplest approach is to divide region into a number of rectangular cells of equal volume and define density as # of points the cell contains
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5 Euclidean Density – Center-based Euclidean density is the number of points within a specified radius of the point
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6 DBSCAN DBSCAN is a density-based algorithm. Density = number of points within a specified radius (Eps) 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 noise point is any point that is not a core point nor a border point.
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DBSCAN: Core, Border, and Noise Points
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DensityCluster-L17 - CSE572/CBS572:DataMining Lecture 17:...

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