Mining frequent itemsets has been widely studied
over the last decade. Past research focuses on mining frequent itemsets from
static databases. In many of the new applications, data flow through the
Internet or sensor networks. It is challenging to extend the mining techniques
to such a dynamic environment. The main challenges include a quick response to
the continuous request, a compact summary of the data stream, and a mechanism
that adapts to the limited resources. In this paper, we develop a novel
approach for mining frequent itemsets from data streams based on a time-sensitive
sliding window model. Our approach consists of a storage structure that
captures all possible frequent itemsets and a table providing approximate
counts of the expired data items, whose size can be adjusted by the available
storage space. Experiment results show that in our approach both the execution
time and the storage space remain small under various parameter settings. In
addition, our approach guarantees no false alarm or no false dismissal to the
results yielded. Theoretic analyses for these guarantees are also provided.