In the traditional segmented data stream clustering algorithm, the inaccuracy of micro-cluster threshold radius T in the online part as well as the oversimplifying of the dealing process with the micro-cluster by the offline part leads to a low clustering quality. In order to break through such limitation, a data stream clustering algorithm on the basis of artificial bee colony optimization for offline part processing is proposed based on the existing dynamic sliding window model. This algorithm consists of two parts:(1) The online part dynamically adjusts the size of the window and improves the value of the micro-cluster threshold radius T according to the length of time that the data stays in the window so as to get micro clustering step by step. (2) The offline part uses the improved bee colony algorithm to continuously adjust dynamically to find the optimal clustering result. The experimental results show that this algorithm not only bears a high clustering quality, but also has fairly good ductility and stability.