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local-density based spatial clustering algorithm with noise

local-density based spatial clustering algorithm with noise. Presenter : Lin, Shu -Han Authors : Lian Duan , Lida Xub , Feng Guo , Jun Lee, Baopin Yan. Information Systems 32 (2007). Outline. Motivation Objective Methodology Experiments Conclusion Comments. Motivation.

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local-density based spatial clustering algorithm with noise

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  1. local-density based spatial clustering algorithmwith noise Presenter : Lin, Shu-Han Authors : LianDuan, LidaXub, FengGuo, Jun Lee, Baopin Yan Information Systems 32 (2007)

  2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

  3. Motivation Clustering DBSCAN (Density Based Spatial Clustering of Applications with Noise) is density-based clustering method. useglobaldensityparametertocharacterizethedatasets.

  4. 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 or a border point. 4

  5. DBSCAN: Core, Border and Noise Points Original Points Point types: core, border and noise Eps = 10, MinPts = 4 5

  6. Objectives • Replaceglobaldensityparameter • Eps • MinPts 6

  7. Methodology– Overview • CorePoint:localoutlierfactor-LOF(p)issmallenough • LOF:thedegreetheobjectisbeingoutlying • LRD:thelocal-densityoftheobject • :Local-densityreachability 7

  8. Methodology– LDBSCAN Ex:LRD(p)/LRD(q)=1.28 Local-densityreachable LRD:thelocal-densityoftheobject reach-distk(p,o)=max{k-distance(o),d(p,o)} 8

  9. Methodology– LDBSCAN LOF:thedegreetheobjectisbeingoutlying 9

  10. Experiments– parameter LOFUB \ MinPts 10

  11. Experiments– parameter Localdensityreachable:pct LRD(q)=0.8 LRD(p)=1 0.8/1.2<1,1!<0.8*1.2,//!Localdensityreachable 0.8/1.5<1,1<0.8*1.5,//Localdensityreachable 11

  12. Experiments–comparewithOPTICS OrderingPointsToIdentifytheClusteringStructure 12

  13. Experiments–comparewithOPTICS TheideaofLOF 13

  14. Conclusions • Globaldensityparametervs.differentlocaldensities • LDBSCAN:Local-density-based

  15. Comments • Advantage • improvesideafromotherapproach • Drawback • It’sstillhardtosettheparameter • Therealdataisnota2-Dproblem • Application • notsuitableforSOM

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