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Finding frequent itemsets in high-speed data streams

Finding frequent itemsets from data streams is one of important tasks of stream data mining. In a time-varying data stream, when the significant change on the frequent itemsets is detected, it is ideal to compute the new frequent itemsets as quickly as possible. Traditional stream data mining algorithms mainly focus on finding the frequent itemsets in one scan, which saves the disk access time. However, the computation cost of processing data is also an important factor in the stream management. In this paper, we propose a new approach that can avoid disk access and meanwhile simplify the computation of data processing. The basic idea is: during the mining of the frequent itemsets, we make every data pass on the significant amount of new coming data rather than scan the old data multiple times. Preliminary experiment results are reported to demonstrate the performance of our approach.