Partitional clustering poses a NP hard search problem for non-trivial problems. While genetic algorithms (GA) have been very popular in the clustering field, particle swarm optimization (PSO) and differential evolution (DE) are rather unknown. In this paper, we report results of a performance comparison between a GA, PSO and DE for a medoid evolution clusterign approach. Our results show that DE is clearly and consistently superior compared to FAs and PSO. both in respect to precision and robustness of the results for hard clustering problems. We conclude that DE rather than GAs should be primarily considered for tackling partitional clustering problems with numerical optimization.
High Performance Clustering with Differential Evolution / Paterlini, S.; T, Krink. - 2:(2004), pp. 2004-2011. (Intervento presentato al convegno Congress on Evolutionary Computation (CEC-2004) tenutosi a Portland, Oregon nel June 2004).
High Performance Clustering with Differential Evolution
S. PATERLINI;
2004-01-01
Abstract
Partitional clustering poses a NP hard search problem for non-trivial problems. While genetic algorithms (GA) have been very popular in the clustering field, particle swarm optimization (PSO) and differential evolution (DE) are rather unknown. In this paper, we report results of a performance comparison between a GA, PSO and DE for a medoid evolution clusterign approach. Our results show that DE is clearly and consistently superior compared to FAs and PSO. both in respect to precision and robustness of the results for hard clustering problems. We conclude that DE rather than GAs should be primarily considered for tackling partitional clustering problems with numerical optimization.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
