Mining massive frequent itemsets based on pfp-growth algorithm

Mining massive frequent itemsets based on pfp-growth algorithm

Jiangyuan, Li Ping

With the development of Internet technology, network data becomes more and more huge, how to excavate effective information becomes the focus of people's research. In recent years, frequent itemsets mining has been paid more and more attention because of its important role in the task of Mining Association rules and related mining. In this paper, based on the research of frequent itemsets mining algorithm in distributed computing environment, the PFP-GROWTH algorithm is improved, and the improved PFP-GROWTH algorithm is implemented and applied through MapReduce programming model, so that users can efficiently obtain all the necessary frequent itemsets from massive data. The experimental results show that the algorithm has high efficiency and scalability for the mass data.

Mining massive frequent itemsets based on pfp-growth algorithm