Fuzzy Clustering by Particle Swarm Optimization
Thomas A. Runkler, Charles M. Katz · 2006
This paper deals with fuzzy clustering by minimizing the fuzzy c-means (FCM) model. We introduce two new methods for minimizing the two reformulated versions of the FCM objective function by particle swarm optimization (PSO). In PSO-V each particle represents a component of a cluster center. In PSO-U each particle represents an unsealed and unnormalized membership value. PSO-V and PSO-U are compared with alternating optimization (AO) and with ant colony optimization (ACO) on two benchmark data sets: the single outlier and the lung cancer data sets. The stochastic methods ACO, PSO-V, and PSO-U are slower than AO, but in each experiment one of the two PSO variants significantly outperforms the other algorithms.