Local stereo matching algorithms are very popular in the recent years. The adaptive support weight algorithms can give high accuracy results such as global methods. This paper proposed a support aggregation approach for stereo matching that computed support weight in sparse support window mask. The improvement from the previous work is that the new support weight can reduce the computation into the fourth of the earlier work and help to reach the optimum correspondence. It means sparse support weight affects the time computation that is needed in stereo matching and optimizes the disparity. This support weight is used to accomplish the stereo matching evaluation using this method.