Dynamic clustering based on quantum-behaved particle swarm optimization

Most clustering algorithm require the number of cluster as a priori knowledge to input, and metrics based on Euclidean distance is good results with only circular clusters. An improved dynamic clustering algorithm was presented, which combines the quantum particle swarm algorithm with k-means algorithm by improving the encoding of quantum particles and the introduction of new distance metric rules. The algorithm has a quantum-behaved particle swarm global search capability. And In order to accelerate the convergence speed, the k-means algorithm is used to optimize every particle. Through the adjustment of the value of the fitness function, our algorithm can search for the optimal clustering number of clusters, so the number of clusters and centers are not subject to subjective factors. Extensive experiments verified the effectiveness of the algorithm.