Frequent Pattern Mining in Data Streams

Frequent pattern mining is a core data mining operation and has been extensively studied over the last decade. Recently, mining frequent patterns over data streams have attracted a lot of research interests. Compared with other streaming queries, frequent pattern mining poses great challenges due to high memory and computational costs, and accuracy requirement of the mining results. In this chapter, we overview the state-of-art techniques to mine frequent patterns over data streams. We also introduce a new approach for this problem, which makes two major contributions. First, this one pass algorithm for frequent itemset mining has deterministic bounds on the accuracy, and does not require any out-of-core summary structure. Second, because the one pass algorithm does not produce any false negatives, it can be easily extended to a two pass accurate algorithm. The two pass algorithm is very memory efficient.