dm5part4 - University of Florida CISE department Clustering...

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University of Florida CISE department Gator Engineering Clustering Part 4 Dr. Sanjay Ranka Professor Computer and Information Science and Engineering University of Florida, Gainesville
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University of Florida CISE department Gator Engineering Data Mining Sanjay Ranka Spring 2011 DBSCAN • DBSCAN is a density based clustering algorithm • Density = number of points within a specified radius ( Eps ) • A point is a core point if it has more than specified number of points ( MinPts ) within Eps – Core point is in the interior of a cluster • A border point has fewer than MinPts within Eps but is in neighborhood of a core point • A noise point is any point that is neither a core point nor a border point
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University of Florida CISE department Gator Engineering Data Mining Sanjay Ranka Spring 2011 DBSCAN: Core, Border and Noise points
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University of Florida CISE department Gator Engineering Data Mining Sanjay Ranka Spring 2011 When DBSCAN works well Original Dataset Clusters found by DBSCAN
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University of Florida CISE department Gator Engineering Data Mining Sanjay Ranka Spring 2011 DBSCAN: Core, Border and Noise points Original Points Eps = 10, Minpts = 4 Point types: Core Border Noise
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University of Florida CISE department Gator Engineering Data Mining Sanjay Ranka Spring 2011 DBSCAN: Determining Eps and MinPts • Idea is that for points in a cluster, there k th nearest neighbors are at roughly the same distance • Noise points have the k th nearest neighbor at at farther distance • So, plot sorted distance of every point to its k th nearest neighbor. (k=4 used for 2D points)
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University of Florida CISE department Gator Engineering Data Mining Sanjay Ranka Spring 2011 Where DBSCAN doesn’t work well Original Points MinPts = 4, Eps = 9.92 Minpts = 4, Eps = 9.75
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University of Florida CISE department Gator Engineering Data Mining Sanjay Ranka Spring 2011 DENCLUE DENsity CLUstEring is a density clustering approach that models the overall density of a set of points as the sum of influence functions associated with each point • DENCLUE is based on kernel density estimation . The goal of kernel density estimation is to describe the distribution of data by a function • For kernel density estimation, the contribution of each point to the overall density function is expressed by an influence (kernel) function . The overall density is then merely the sum of the influence functions associated with each point
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University of Florida CISE department Gator Engineering Data Mining Sanjay Ranka Spring 2011 DENCLUE • The resulting overall density functions will have local peaks, i.e. local density maxima, and these local peaks can be used to define clusters – For each point, a hill climbing algorithm finds the nearest peak associated with that point, and set of all data points associated with a peak form a cluster – However, if the density at a local peak is too low, then the points in the associated cluster are labeled as noise and discarded – Similarly, if two peaks are connected by a path of data
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This note was uploaded on 11/13/2011 for the course CIS 4930 taught by Professor Staff during the Spring '08 term at University of Florida.

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dm5part4 - University of Florida CISE department Clustering...

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