Optimized Apriori Algorithm Description and Application in Vehicle Fault Phenomenon Correlation Analysis

Abstract

This paper proposes an optimized Apriori data correlation analysis algorithm and describes it, and evaluates the results of the model in the correlation analysis of vehicle fault phenomena. The algorithm reduces the load of I/O interface and improves the computational efficiency by optimizing the number of frequent itemset and pruning In the data mining process. The algorithm is applied to the correlation analysis of common faults in vehicle engine system. The optimized traversal method enables it to quickly find frequent patterns, correlations and causal structures among fault data sets, and detect potential correlations among faults, which achieves better application effect.