Parallel architecture for implementation of frequent itemset mining using FP-growth

Frequent itemset mining is a fundamental step in analysis of big data where correlation among the raw data in deemed necessary. In modern era the amount of data available for processing has grown exponentially, making it a stepper task for mining algorithms to provide solution in a timely manner. The software implementations are normally not efficient in handling such datasets thus focus on parallel architecture seems imminent. In this paper we propose a Multi-Processor based sequentially unfolded architecture for implementation of FP-Growth algorithm. The proposed framework exploits the inherent parallelism available in the FP-Growth algorithm such that N-processing entities (PEs) can work in a collaborative environment. The processing entities work in an independent manner in parallel and largely interchange data at the close of each iteration. The overall architecture is modular which permits scalability of the design with regards to the number of parallel processing entities. The performance of the framework is evaluated using benchmark datasets and their results show a linear increase in the speedup of our proposed framework with increase in PEs.