RETRACTED ARTICLE: The utilization of rough set theory and data reduction based on artificial intelligence in recommendation system

Abstract

To improve the effectiveness of allocating the education environmental resources in college music, an allocation model for education environmental resource based on rough set and data reduction is proposed. First, an allocation model of risk preference for education environmental resource based on rough set and data reduction classifier is developed. This model divides risk preference into the existing risk preference and non-existent risk preference. Also, it trains and optimizes the data set for the allocation of education environmental resources by rough set and data reduction classifier. Second, as the rough set and data reduction classifier may fail sometimes in predicting the allocation of education environmental resource, the fuzzy clustering based on rough set is optimized, and combined with the data reduction theory to enhance the effectiveness of allocating the education environmental resources. Finally, based on the simulation research on the allocation model of education environmental resources, the proposed algorithm can obtain a more reasonable allocation of education environmental resources, which embodies the effectiveness of the algorithm. The data reduction classifier of a rough set is used to train and optimize the data set of the allocation of education environmental resources, and visual c++ is used to realize the allocation of education environmental resources. The simulation results show that the algorithm can effectively allocate education environmental resources. The model takes into account the characteristics of the incompatibility between the evaluation of historical data and the complex index system in the evaluation decision-making. It can divide the evaluation decision-making table reasonably, and realize the objective calculation of the weights of each evaluation index based on the rough set condition through hierarchical calculation.