Continuous Prediction of Closed Frequent Itemsets from High Speed Distributed Data Streams using Parallel Mining on Manifold Windows with Varying Size

Continuous prediction of closed frequent itemsets from high speed distributed data streams is an active research work, which is because of the conflict to the process time taken to perform mining consistent itemsets from current records and high alacrity transmission time in data streams. By the motivation gained from our earlier proposed models, here we devised a novel closed frequent itemset mining model for high speed distributed data streams. The said model is referred as Parallel Closed Frequent Itemsets Mining (PCFIM) over High Speed Distributed Data streams by Manifold Varying Size Windows (MVSW). The results obtained from experiments are significant to prove that the proposed PCFIM is scalable and robust on high speed data streams and miles ahead over existing bench mark models