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Enhancing Density-Based Spatial Clustering with Noise: A Local Density Approach

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This paper presents an improved version of the DBSCAN algorithm, referred to as LDBSCAN, which incorporates local density metrics to enhance clustering performance. Traditional DBSCAN relies heavily on global parameters, Eps and MinPts, which may not be optimal for all datasets. The proposed methodology uses local outlier factors to determine the degree of outlyingness for points, enabling better delineation of clusters and noise. Experiments illustrate the advantages of LDBSCAN over traditional techniques, showcasing its applicability in more complex, multidimensional data scenarios.

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Enhancing Density-Based Spatial Clustering with Noise: A Local Density Approach

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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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