Deep mining of communication information association based on discrete Fourier transform

Abstract

In order to better analyze communication information and improve the efficiency of communication information association mining, a discrete Fourier algorithm is proposed to extract association rules from information flow data with different time granularity. With the help of the MapReduce parallel programming model, a deep mining algorithm on the association of communication information is designed. Firstly, the data is linearly normalized, and the algorithm model of data depth mining is proposed according to the discrete Fourier transform. The data source uses the message middleware Kafka, and the intermediate results are stored in the memory database redis through the serialization technology. By using the structural information and comparison algorithm of the database, K pairs with the largest Pearson correlation coefficient are searched, association rules are mined, meaningless items are filtered out, construct an association deep mining model to realize rapid mining of association rules related to tasks. Experimental results and analysis show that the algorithm is efficient and stable in discovering association rules from high-speed information data flow, and supports different time granularity.