Name
Abstract | ||
|---|---|---|
While network clustering is traditionally accomplished just relying on the topology of the network, the new traffic-aware clustering approach employs traffic matrices to take into account the intensity of the relationship between nodes. In the context of traffic-aware clustering we propose a new Evolutionary Clustering algorithm and compare it with the Spectral Filtering algorithm. We compare them using both the Modularity and the Traffic-aware Scaled Coverage metrics, and two real-world datasets, each made of 1000 traffic matrices, respectively from Abilene and Géant networks. Our experiments show that Evolutionary Clustering performs better on all traffic matrices, excepting a minor number of traffic matrices in the Abilene network when the Modularity metric is employed. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IWCMC.2011.5982607 | IWCMC |
Keywords | Field | DocType |
evolutionary computation,network clustering,traffic matrices,evolutionary clustering algorithm,traffic-aware scaled coverage metrics,network topology,telecommunication network topology,genetic algorithms,algorithm theory,spectral filtering algorithm,modularity metric,genetics,evolutionary algorithm,measurement,filtering,genetic algorithm,clustering algorithms | Canopy clustering algorithm,Data mining,CURE data clustering algorithm,Clustering high-dimensional data,Data stream clustering,Correlation clustering,Computer science,Hierarchical clustering of networks,Consensus clustering,Cluster analysis | Conference |
ISSN | ISBN | Citations |
2376-6492 | 978-1-4244-9539-9 | 2 |
PageRank | References | Authors |
0.41 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sancho Salcedo-Sanz | 1 | 580 | 71.21 |
| Maurizio Naldi | 2 | 285 | 47.98 |
| Leopoldo Carro-Calvo | 3 | 79 | 7.45 |
| Luigi Laura | 4 | 305 | 36.85 |
| Antonio Portilla-Figueras | 5 | 147 | 19.07 |
| Giuseppe F. Italiano | 6 | 2364 | 254.07 |