Fast Processing of Conversion Time Data Flow in Cloud Computing via Weighted FP-Tree Mining Algorithms

The data distribution in the data streams usually changes dynamically with time. Traditional mining algorithms based on transaction are difficult to establish the correlation between time characteristics and relationship features, thus making the results inaccurate. By analyzing the problems in the processing of time data stream, we put forward the concept of time gap degrees and design a mining algorithms based on weighted FP-Tree. We introduce the concept of FP-Tree node weights to transform the time data dynamically and excavate the data stream association rules. The experiments performed on the actual data set show that the algorithm can improve the recall and precision while consumes comparable computational time.