Application of Intelligent Terminal Access Technology under Data Mining in Fog Computing Map Reduce Coding Simulation Analysis

Abstract

To cope with the computing needs under the rapidly increasing amount of data and improve the computer's operating efficiency, this paper studies the data computing methods. First, the Hadoop big data processing technology and the working principle of MapReduce are studied. Second, based on MapReduce, the Apriori association rule algorithm is improved, and a minimum support threshold algorithm is proposed. At the same time, the C4.5 decision tree algorithm (DTA) is improved based on MapReduce. Finally, the operation efficiency of the improved algorithm is analyzed. The improved algorithm of Apriori based on MapReduce can shorten the operation time of the algorithm effectively and reduce the computing complexity. As the number of clusters increases, the operating time of the DTA based on MapReduce's cross-blocking can be effectively reduced. Time consumed is shortened, and the algorithm is verified to be highly scalable and parallel. Through this study, it is found that the intelligent terminal access technology based on data mining can decrease the data calculating time in fog computing MapReduce coding simulation analysis, and improve the data calculation efficiency and computer operation efficiency.