  The challenges with respect to mining frequent items over data streaming
engaging variable window size and low memory space are addressed in this
research paper. To check the varying point of context change in streaming
transaction we have developed a window structure which will be in two levels
and supports in fixing the window size instantly and controls the
heterogeneities and assures homogeneities among transactions added to the
window. To minimize the memory utilization, computational cost and improve the
process scalability, this design will allow fixing the coverage or support at
window level. Here in this document, an incremental mining of frequent
item-sets from the window and a context variation analysis approach are being
introduced. The complete technology that we are presenting in this document is
named as Mining Frequent Item-sets using Variable Window Size fixed by Context
Variation Analysis (MFI-VWS-CVA). There are clear boundaries among frequent and
infrequent item-sets in specific item-sets. In this design we have used window
size change to represent the conceptual drift in an information stream. As it
were, whenever there is a problem in setting window size effectively the
item-set will be infrequent. The experiments that we have executed and
documented proved that the algorithm that we have designed is much efficient
than that of existing.
