Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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)

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-dist k (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) = 0.8 LRD(p) = 1 0.8/1.2<1, 1!<0.8*1.2, // !Local density reachable 0.8/1.5<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. Conclusions Global density parameter vs. different local densities LDBSCAN: Local-density-based 14

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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 15