Comparative Study of Frequent Itemset Mining Techniques on Graphics Processor

Abstract

Frequent itemset mining (FIM) is a core area for many data mining applications as association rules computation, clustering and correlations, which has been comprehensively studied over the last decades. Furthermore, databases are becoming gradually larger, thus requiring a higher computing power to mine them in reasonable time. At the same time, the improvements in high performance computing platforms are transforming them into massively parallel environments equipped with multi-core processors, such as GPUs. Hence, fully operating these systems to perform itemset mining poses as a challenging and critical problems that addressed by various researcher. We present survey of multi-core and GPU accelerated parallelization of the FIM algorithms