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.
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.
Intelligent Database Systems Lab Methodology-SOMs Adapted BMU Updating BMU RFi and RFij
Intelligent Database Systems Lab Minimum is better Maximum is better nearest 1 is better Methodology-Number of cluster
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 Linkage’s time complexity = O(p^2*d) Average Linkage’s time complexity = O(p^3*d)
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.
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.