An Efficient Approximate Approach to Mining Frequent Itemsets over High Speed Transactional Data Streams

A data stream is a massive and unbounded sequence of data elements that are continuously generated at a fast speed. Compared with traditional data mining, knowledge discovery in data streams is more challenging since several requirements need to be satisfied. In this paper we propose a mining algorithm for finding frequent itemsets over a transactional data stream. Unlike most of existing algorithms, our method works based on the theory of Approximate Inclusion-Exclusion to approximate the itemsets´ counts. Some techniques are designed and integrated into the algorithm for performance improvement. And the performance of the proposed algorithm is tested and analyzed through several experiments.