Abstract
Frequent itemset mining is widely used as a fundamental data mining technique. Recently, there have been proposed a number of disk-based methods. However, the existing methods still do not have a good scalability due to large-scale intermediate data and non-trivial disk I/Os. We propose SSDMiner, a new fast and scalable disk-based method for frequent itemset mining that is based on Apriori-like method and has no intermediate data and small disk I/O overheads by exploiting SSD. We propose a concept of bitmap chunks for storing transactional database in disks and a fast support counting based on bitmap chunks. Through experiments, we demonstrate that SSDMiner has the enhanced scalability and the good performance similar to that in memory-based methods with robustness.
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