Extension of graph clustering algorithms based on SCAN method in order to target weighted graphs
Journal: Scholarship at UWindsor (University of Windsor)
Year: 2012

In this thesis we evaluate current neighbour-based graph clustering algorithms: SCAN, DHSCAN, and AHSCAN. These algorithms possess the ability to identify special nodes in graphs such as hubs and outliers. We propose and extension for each of these in order to support weighted edges. We further implemented two graph generating frameworks to create test cases. In addition we used a graph derived from the ENRON email log. We also implemented a Fast Modularity clustering algorithm, which is conside