local-density based spatial clustering algorithm with noise
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Intelligent Database Systems Lab
N.Y.U.S.T.I. M.
local-density based spatial clustering algorithm
with noise
Presenter : Lin, Shu-HanAuthors : Lian Duan, Lida Xub, Feng Guo, Jun Lee, Baopin Yan
Information Systems 32 (2007)
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Intelligent Database Systems Lab
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Outline
Motivation Objective Methodology Experiments Conclusion Comments
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Intelligent Database Systems Lab
N.Y.U.S.T.I. M.Motivation
DBSCAN (Density Based Spatial Clustering of Applications with Noise) is density-based clustering method.
use global density parameter to characterize the datasets.
Clustering
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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 or a border point.
DBSCAN
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Intelligent Database Systems Lab
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Original Points Point types: core, border and noise
Eps = 10, MinPts = 4
DBSCAN: Core, Border and Noise Points
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Intelligent Database Systems Lab
N.Y.U.S.T.I. M.Objectives
Replace global density parameter Eps MinPts
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Intelligent Database Systems Lab
N.Y.U.S.T.I. M.Methodology – Overview
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Core Point: local outlier factor - LOF(p) is small enough LOF: the degree the object is being outlying LRD: the local-density of the object :Local-density reachability
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Intelligent Database Systems Lab
N.Y.U.S.T.I. M.Methodology – LDBSCAN
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Local-density reachable
LRD: the local-density of the object
reach-distk (p, o) = max{k-distance(o), d(p, o)}
Ex: LRD(p)/LRD(q)=1.28
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Intelligent Database Systems Lab
N.Y.U.S.T.I. M.Methodology – LDBSCAN
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LOF: the degree the object is being outlying
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Intelligent Database Systems Lab
N.Y.U.S.T.I. M.Experiments – parameter
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LOFUB \
MinPts
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Intelligent Database Systems Lab
N.Y.U.S.T.I. M.Experiments – parameter
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Local density reachable:pct
LRD(q) = 0.8LRD(p) = 10.8/1.2<1, 1!<0.8*1.2, // !Local density reachable0.8/1.5<1,1 <0.8*1.5, // Local density reachable
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Intelligent Database Systems Lab
N.Y.U.S.T.I. M.Experiments – compare with OPTICS
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Ordering Points To Identify the Clustering Structure
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Intelligent Database Systems Lab
N.Y.U.S.T.I. M.Experiments – compare with OPTICS
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The idea of LOF
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Intelligent Database Systems Lab
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Conclusions
Global density parameter vs. different local densities LDBSCAN: Local-density-based
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Comments
Advantage improves idea from other approach
Drawback It’s still hard to set the parameter The real data is not a 2-D problem
Application not suitable for SOM