Fullscale data mining, such as in cluster problems, requires large numbers of computations. A parallel cluster algorithm for selfadaptive particle swarm optimization was proposed to deal with this problem. The proposed parallel particle swarm optimization algorithm reduced the impact of the initial conditions via parallel searches of the globally best position amongst a varied population. Task parallelization and partially asynchronous communication of the algorithm were employed to decrease computing time. Furthermore, if combined with the characteristics of selfadaptive and dynamical optimization parameters of the parallel particle swarm algorithm, the problems of particle mobility loss and the end of evolution could be dealt with successfully. When modified thusly, the algorithm maintains individual diversity and restrains degeneration. The simulation experiments indicate the algorithm helps increase computing speed and improve cluster quality.