  Mining frequent itemsets through static Databases has been extensively
studied and used and is always considered a highly challenging task. For this
reason it is interesting to extend it to data streams field. In the streaming
case, the frequent patterns' mining has much more information to track and much
greater complexity to manage. Infrequent items can become frequent later on and
hence cannot be ignored. The output structure needs to be dynamically
incremented to reflect the evolution of itemset frequencies over time. In this
paper, we study this problem and specifically the methodology of mining
time-sensitive data streams. We tried to improve an existing algorithm by
increasing the temporal accuracy and discarding the out-of-date data by adding
a new concept called the "Shaking Point". We presented as well some experiments
illustrating the time and space required.
