GANC: Greedy Agglomerative Normalized Cut
Student: Seyed Salim Tabatabaei, M. Eng Student
Supervisor: Prof. Mark Coates
Collaborator: Prof. Mike Rabbat
Description:
GANC is a graph clustering algorithm that aims to minimize the normalized cut criterion and has a model order selection procedure. The performance of GANC is comparable to spectral approaches in terms of minimizing normalized cut. However, unlike spectral approaches, the proposed algorithm scales to graphs with millions of nodes and edges. GANC consists of three components that are processed sequentially: a greedy agglomerative hierarchical clustering procedure, model order selection, and a local refinement.