关键词: 汽车售后服务, 故障数据, FP-growth改进算法, 剪枝策略, 均衡分组

Abstract: Aiming at the characteristics of large amount of fault data and rapid growth in the after-sales service of automobile industry chain platform, and the defects of traditional FP-growth algorithm in processing massive data, an improved FP-growth algorithm based on MapReduce is proposed to mine the association relationship in the after-sales fault information. The algorithm combines the advantages of pruning strategy and balanced grouping strategy. The pruning strategy is used to reduce the number of iterations of item set mining. Based on the balanced grouping algorithm, the load balancing of parallel frequent pattern mining process is realized. The experimental results show that the proposed algorithm performs better. Taking the historical fault data of the after-sales service of automobile industry chain collaborative platform as a sample, the important faults with high frequency of occurrence and the associated faults with high probability of simultaneous failure are discovered.

Key words: automobile after-sales service, fault data, FP-growth improved algorithm, pruning strategy, balanced grouping