Bat-Cluster: A Bat Algorithm-based Automated Graph Clustering Approach

Zakaria Boulouard (Ibn Zohr University)
Amine El Haddadi (Paul Sabatier University)
Fadwa Bouhafer (Mohamed 1st University)
Anass El Haddadi (Mohamed 1st University)
Lahcen Koutti (Ibn Zohr University)
Bernard Dousset (Paul Sabatier University)

International Journal of Electrical and Computer Engineering
Vol 8, No 2: April 2018

Defining the correct number of clusters is one of the most fundamental tasks in graph clustering. When it comes to large graphs, this task becomes more challenging because of the lack of prior information. This paper presents an approach to solve this problem based on the Bat Algorithm, one of the most promising swarm intelligence based algorithms. We chose to call our solution, “Bat-Cluster (BC).” This approach allows an automation of graph clustering based on a balance between global and local search processes. The simulation of four benchmark graphs of different sizes shows that our proposed algorithm is efficient and can provide higher precision and exceed some best-known values.