Application of Parallel Annealing Particle Clustering Algorithm in Data Mining

With development of the computer technology, the large-scale calculation problems are often appeared in the network, it needs a lot of system resources and support of hardware, it often bring troubles in engineering optimization, so it needs apply the method such as the group's global optimization method and its improved algorithm to obtain reliable results in the computer system. In the study, it proposes a kind of particle swarm optimization based on parallel annealing parallel clustering algorithm, it is a new global optimization algorithm and it is especially suitable for continuous variable problem. In the engineering field, it can be used in large-scale computational problems; it is based on the method of group, and has parallelism abilty. In the parallel particle swarm optimization algorithm, the particle swarm can reduce the consumption of calculation time. The experimental results show that the Multipoint interface (MPI) communication used in annealing parallel particle swarm optimization algorithm not only can reduce the computing time of particle swarm, but also improve the clustering quality, stronger effectiveness algorithm is verfied.