local-density based spatial clustering algorithm with noise

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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Outline

Motivation Objective Methodology Experiments Conclusion Comments

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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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Original Points Point types: core, border and noise

Eps = 10, MinPts = 4

DBSCAN: Core, Border and Noise Points

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N.Y.U.S.T.I. M.Objectives

Replace global density parameter Eps MinPts

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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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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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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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N.Y.U.S.T.I. M.Experiments – parameter

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LOFUB \

MinPts

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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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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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N.Y.U.S.T.I. M.Experiments – compare with OPTICS

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The idea of LOF

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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