Focusing on the stream data real time performance, orderliness, and high dimension, a streaming data clustering algorithm based on cooperative particle swarm optimization is proposed, which divides sequential stream data into several data subsets according to the time stamp. For any data subset, the high-dimensional clustering problem is firstly transformed into the low dimensional sub-problem with only one class center. Then, one sub-swarm optimizes one clustering sub-problem independently, and all the sub-swarms cooperate with each other to find the whole solution of the streaming data. Moreover, in order to enhance the speed of tracking the environment changes, a forecast strategy is designed to predict the change trend of class centers. In order to avoid multiple sub-swarms repeatedly searching for the same class center, a merging strategy of similar sub-swarms is proposed. Finally, the proposed algorithm is applied to multiple data sets, and experimental results
show the effectiveness.