Parallelizing Frequent Itemset Mining Process using High Performance Computing

Data is growing at an enormous rate and mining this data is becoming a herculean task. Association Rule mining is one of the important algorithms used in data mining and mining frequent itemset is a crucial step in this process which consumes most of the processing time. Parallelizing the algorithm at various levels of computation will not only speed up the process but will also allow it to handle scalable data. This paper proposes a model to parallelize the frequent itemset mining process without additional load of using multiprocessors using high performance computing. GPU have been used which offer better performance at significantly low cost and are also energy efficient as compared to multi-core multiprocessors.