Mining Frequent Patterns in Data Streams at Multiple Time Granularities
Chris R. Giannella, Jiawei Han, Xifeng Yan, Philip S. Yu · 2002
Although frequent-pattern mining has been widely studied and used, it is challenging to extend it to data streams. Compared to mining from a static transaction data set, the streaming case has far more information to track and far greater complexity to manage. Infrequent items can become frequent later on and hence cannot be ignored. The storage structure needs to be dynamically adjusted to reflect the evolution of itemset frequencies over time.