Mining Recent Frequent Itemsets in Data Streams with Optimistic Pruning
نویسندگان
چکیده
A data stream is a massive unbounded sequence of transactions continuously generated at a rapid rate, so how to process the transactions as fast as possible in the limited memory becomes an important problem. Although it has been studied extensively, most of the existing algorithms maintain a lot of infrequent itemsets, which causes huge space usage and inefficient update. In this paper, a new algorithm, called OPFIstream, is proposed to mine all accurate frequent itemsets from sliding window over data streams. The OPFI-stream algorithm maintains a dynamically selected set of itemsets in a prefix-tree based data structure. By using an optimistic pruning strategy, quite a lot of infrequent itemsets can be pruned during the construction and updates. Mining all frequent itemsets with accurate frequencies is just to traverse the tree. Experiments show that the performance is improved greatly even when the user-specified minimum support threshold is small.
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