  The need to analyze information from streams arises in a variety of
applications. One of its fundamental research directions is to mine sequential
patterns over data streams. Current studies mine series of items based on the
presence of the pattern in transactions but pay no attention to the series of
itemsets and their multiple occurrences. The pattern over a window of itemsets
stream and their multiple occurrences, however, provides additional capability
to recognize the essential characteristics of the patterns and the
inter-relationships among them that are unidentifiable by the existing
presence-based studies. In this paper, we study such a new sequential pattern
mining problem and propose a corresponding sequential miner with novel
strategies to prune the search space efficiently. Experiments on both real and
synthetic data show the utility of our approach.
