Time Sensitive Data Stream on Frequent Patterns in Data Mining

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 An existing or viewing algorithm by increasing the temporal accuracy is too cheque it in thread discarding the out-of-date data by adding a new method is called to the "Shaking Point".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