The influence of computer network technology on national income distribution under the background of social economy

摘要

Data mining algorithm is widely used in data management. In view of the current situation that the edge computing mode based on computer network technology is applied to data management, this paper studies the optimization and improvement of the classic Apriori algorithm and K-means algorithm respectively, improves the operation efficiency by optimizing the candidate set, enhances the clustering effect by determining the initial clustering center, and applies the improved algorithm to the national income management which contains 10 attributes. The algorithm performance test shows that the improved Apriori algorithm can generate frequent itemsets in a shorter time, which is about 2000 ms when the minimum support threshold is 30%- 90%, while the original algorithm is about 4000 Ms. K-means algorithm is relatively sparse in the same type of distribution, while im-k-means algorithm is relatively aggregated. According to the confidence level and related attributes, the order of the influence on salary level from large to small is marital status, education level and time, working time and nature, and the corresponding confidence levels are about 0.95, 0.84 and 0.80 respectively. The improved Apriori algorithm and im-k-means algorithm can well show the relationship between education level and salary level. The research results make a great contribution to the application of computer network technology in data management, and also provide a scientific and reasonable method for the income classification of population.