Application of an Improved Association Rule Algorithm in Rural Development Assessment in China

Abstract

Association rules based on big data are derived from shopping basket analysis, which can deeply explore the association between things, and have been widely used in many fields. The traditional algorithm of association rules has several shortcomings, a new improved algorithm of association rules was proposed in this paper, which combines the precise advantages of Bayesian classifier with the full-scale characteristics of big data technology, and was applied to the rural development assessment process. After 2020, China’s rural industrial structure will undergo great changes, and environmental protection to drive economic development is an important part of it. The new association rules can be better adapted to the needs of the assessment. Through comparison, it can be seen that the confidence results obtained by the new association rules are more accurate than Pearson correlation coefficient. Finally, the association rules among CPI, per capita consumption expenditure and per capita disposable income are analysed, and suggestions for rural development have also been made.