Frequent pattern mining on message passing multiprocessor systems

Extraction of frequent patterns in transaction-oriented database is crucial to several data mining tasks such as association rule generation, time series analysis, classification, etc. Most of these mining tasks require multiple passes over the database and if the database size is large, which is usually the case, scalable high performance solutions involving multiple processors are required. This paper presents an efficient scalable parallel algorithm for mining frequent patterns on parallel shared nothing platforms. The proposed algorithm is based on one of the best known sequential techniques referred to as Frequent Pattern (FP) Growth algorithm. Unlike most of the earlier parallel approaches based on different variants of the Apriori Algorithm, the algorithm presented in this paper does not explicitly result in having entire counting data structure duplicated on each processor. Furthermore, the proposed algorithm introduces minimum communication (and hence synchronization) overheads by efficiently partitioning the list of frequent elements list over processors. The experimental results show scalable performance over different machine and problem sizes. The comparison of implementation results with existing parallel approaches show significant gains in the speedup. On an 8-processor machine, we report an average speedup of 6 for different problem sizes.