Continuous Prediction of Closed Frequent Itemsets From High Speed Distributed Data Streams Using Parallel Mining on Manifold Windows With Varying Size

Provided by: International Journal of Computer Applications
Topic: Data Management
Format: PDF
Continuous prediction of closed frequent item sets 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 item sets from current records and high alacrity transmission time in data streams. By the motivation gained from the authors' earlier proposed models, here they devised a novel closed frequent item set mining model for high speed distributed data streams. The said model is referred as Parallel Closed Frequent Item sets Mining (PCFIM) over high speed distributed data streams by Manifold Varying Size Windows (MVSWs).

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