Association rule-based algorithm for repairing missing values in transmission data

Due to the dynamic nature of the relationship between different sequences of transmission data, the use of static association rules to fit missing values in sequences often has the disadvantage of low consistency between repair values and missing values. In response to this, research has been conducted on a transmission data missing value repair algorithm based on association rules. The prefix tree of the Can tree merges as many identical transmission data items as possible, and embeds a Can tree containing newly added transmission data sequence items into the transmission data Can tree within the time window, outputting transmission data association rules containing dynamic attributes. Establish spatiotemporal maps of frequent itemsets corresponding to different data sequences using association rules, and fit missing values based on the correlation between data. On the test dataset, the MRE between the repaired values and the actual values under different data missing rates remained stable within 25.0%, and the consistency between the repaired values and the actual missing values was mainly concentrated above 96.0%, reaching a high level.