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)

Intelligent Database Systems Lab

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

2

Outline

Motivation Objective Methodology Experiments Conclusion Comments

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

Intelligent Database Systems Lab

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

4

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

4

Intelligent Database Systems Lab

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

Original Points Point types: core, border and noise

Eps = 10, MinPts = 4

DBSCAN: Core, Border and Noise Points

5

Intelligent Database Systems Lab

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

Replace global density parameter Eps MinPts

6

Intelligent Database Systems Lab

N.Y.U.S.T.I. M.Methodology – Overview

7

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

Intelligent Database Systems Lab

N.Y.U.S.T.I. M.Methodology – LDBSCAN

8

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

Intelligent Database Systems Lab

N.Y.U.S.T.I. M.Methodology – LDBSCAN

9

LOF: the degree the object is being outlying

Intelligent Database Systems Lab

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

10

LOFUB \

MinPts

Intelligent Database Systems Lab

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

11

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

Intelligent Database Systems Lab

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

12

Ordering Points To Identify the Clustering Structure

Intelligent Database Systems Lab

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

13

The idea of LOF

Intelligent Database Systems Lab

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

14

Conclusions

Global density parameter vs. different local densities LDBSCAN: Local-density-based

Intelligent Database Systems Lab

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

15

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