Abstract: In the power Internet of Things, user-side data is in a relatively isolated position, which makes it more difficult to mine data association rules. Therefore, a deep mining method of user-side data in the power Internet of Things based on association rule mapping is proposed. Based on the directed graph structure of user-side data mesh topology, the association mapping relationship among data sets is analyzed according to the association attribute group. And the association rules among data sets are mined using the correlation matrix. Extreme value standardization strategy and radial basis function neural network are introduced, and the dimensionless method and discrete clustering method are built. Through the hidden layer neural network is obtained. According to the K-means clustering process, data preprocessing, data types according to the different users of dominant and recessive side matrix, score matrix and users-project scale, deep data mining is realized. Experimental results show that this method can complete the mining task in a relatively short time, the processing effect of different data sets is better, and the data depth mining can be completed in a small memory space.

Key words: association rules； , association mapping； , power Internet of things； , user side； , data mining