Slide 1

Intelligent Database Systems Lab Presenter : WU, MIN-CONG Authors : KADIM TASDEMIR, PAVEL MILENOV, AND BROOKE TAPSALL 2011,IEEE Topology-Based Hierarchical Clustering of Self-Organizing Maps Slide 2 Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments Slide 3 Intelligent Database Systems Lab Motivation Hierarchical clustering various distance-based similarity measures that have some flaw. 1. sensitivity to inhomogeneous within-cluster density distributions, noise or outliers. 2. depend on the cluster centroids and dispersion around these centroids. Slide 4 Intelligent Database Systems Lab Objectives we employ average linkage for hierarchical clustering of prototypes based on CONN so that at each agglomeration step we merge the pair with maximum average between cluster connectivity that method CONN linkage, and add a new similarity criteria CONN_Index. Slide 5 Intelligent Database Systems Lab Methodology-SOMs Adapted BMU Updating BMU RFi and RFij Slide 6 Intelligent Database Systems Lab Methodology-Connectivity Martix P1P2P3 P1035 P2302 P3520 from CONN(P1,P2) = 3 CONN(P1,P3) = 5 CONN(P2,P3) = 2 CONN(P3,P2) = 2 CONN(P3,P1) = 5 CONN(P2,P1) = 3 CONN P1P2P3 P1035 P2302 P3520 CONN P1P2P3 P1013 P2201 P3210 CADJ CONN=CADJ(p1,p2)+CADJ(p2,p1) =1+2 =3 Slide 7 Intelligent Database Systems Lab Methodology-CONN Linkage S1S2S3S4 S10126 S21034 S32305 S46450 Similary matrix S2S3 S203 S330 Delete Add S2S3N1 S2035 S3304 N1540 Slide 8 Intelligent Database Systems Lab Minimum is better Maximum is better nearest 1 is better Methodology-Number of cluster Slide 9 Intelligent Database Systems Lab Methodology-Applicability and Complexity of the Algorithm represent the data topology Delaunay graph is to have dense enough prototypes. occasionally CONN Linkages time complexity = O(p^2*d) Average Linkages time complexity = O(p^3*d) Slide 10 Intelligent Database Systems Lab Experiment Slide 11 Intelligent Database Systems Lab Experiment Slide 12 Intelligent Database Systems Lab Experiment Slide 13 Intelligent Database Systems Lab Experiment Slide 14 Intelligent Database Systems Lab Experiment Slide 15 Intelligent Database Systems Lab Experiment Slide 16 Intelligent Database Systems Lab Experiment Slide 17 Intelligent Database Systems Lab Conclusions CONN linkage produces partitionings better than the ones obtained by distance-based linkages. Conn_Index based on CONN graph provided better decisions than other indices in the study reported in this paper. Slide 18 Intelligent Database Systems Lab Comments Advantages CONN_Index provides the best partitioning for the preset number of clusters. CONN linkage is mainly proposed for accurate clustering of remote sensing imagery Applications hierarchical clustering.