Mining Frequent Itemsets (MFI) Over Data Streams: Variable Window Size (VWS) by Context Variation Analysis (CVA) of the Streaming Transactions

摘要

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