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
