改进的并行关联规则增量挖掘算法.

In the big data environment, the Can-tree based on incremental association rule algorithm has problems such as too much space occupation of the tree structure, the efficiency of frequent pattern mining is poor, and the parallelization performance of MapReduce cluster is insufficient. Aiming at these problems, this paper proposed the MR-PARIRM. Firstly, it designed a RS-SIM to merge similar items in the dataset, and constructed Can-tree based on the merged data, thereby reducing the space occupation of the tree structure. Secondly, this paper proposed an MPS to prune and merge the propagation paths in the tree structure, thereby compressing the frequent pattern search space to speed up frequent item mining. Finally, MRP ARIRM used the DSS to dynamically schedule the computing tasks in the heterogeneous MapReduce cluster, thereby implementing the load balance and effectively improving the parallel computing capabilities of the cluster. The final experimental simulation results show that MR-PARIRM has relatively better performance in the big data environment and is suitable for parallel processing of large-scale data. [ABSTRACT FROM AUTHOR]