DBCA mining algorithm is used to find frequent item sets over the transactional data stream. The proposed method works based on the theory of Approximate Inclusion–Exclusion.It extracts base information from data streams in a dynamic way and keeps the base information on a data stream with the size concerning the average length n of transactions. Without incrementally maintaining the overall synopsis of the stream, we can approximate the item set counts according to certain kept information and the counts bounding technique.