A New Parallel Algorithm for Frequent Pattern Mining

摘要

Frequent patterns mining is one of the important topics that have been discussed recently in the field of data mining. Frequent patterns are fundamental in generating association rules, time series, etc. Most frequent pattern mining algorithms can be classified into two categories: Generate-and-test approach (Apriori-like) and pattern growth approach (FP-tree). Frequent pattern mining is based on the FP-tree approach and it only needs two database scans. FIUT algorithm can reduce the computational time of FP-growth algorithm. For pattern growth methods, the execution time increases rapidly when the database size increases or when the given support is small. Therefore, parallel-distributed computing is a good strategy for solving this problem. In this field two parallel mining algorithms proposed, BTP-tree and TPFP-tree. In this paper, we propose TP-FIUT algorithm that parallelizes FIUT. The method of our algorithm is similar to parallelized FP-growth method. We show that if the database size is large, the run time of our proposed algorithm on homogeneous and heterogeneous computing clusters is less than other algorithms and our proposed algorithm has a better load balance capability than TPFP-tree and PFP-tree and BTP-tree on a multi-cluster grid.