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GMiner: A fast GPU-based frequent itemset mining method for large-scale data
- GMiner: A fast GPU-based frequent itemset mining method for large-scale data
- Chon, Kang Wook; Hwang, Sang Hyun; Kim, Min Soo
- DGIST Authors
- Kim, Min Soo
- Issue Date
- Information Sciences, 439-440, 19-38
- Article Type
- Author Keywords
- Frequent itemset mining; Graphics processing unit; Parallel algorithm; Workload skewness
- Frequent itemset mining is widely used as a fundamental data mining technique. However, as the data size increases, the relatively slow performances of the existing methods hinder its applicability. Although many sequential frequent itemset mining methods have been proposed, there is a clear limit to the performance that can be achieved using a single thread. To overcome this limitation, various parallel methods using multi-core CPU, multiple machine, or many-core graphic processing unit (GPU) approaches have been proposed. However, these methods still have drawbacks, including relatively slow performance, data size limitations, and poor scalability due to workload skewness. In this paper, we propose a fast GPU-based frequent itemset mining method called GMiner for large-scale data. GMiner achieves very fast performance by fully exploiting the computational power of GPUs and is suitable for large-scale data. The method performs mining tasks in a counterintuitive way: it mines the patterns from the first level of the enumeration tree rather than storing and utilizing the patterns at the intermediate levels of the tree. This approach is quite effective in terms of both performance and memory use in the GPU architecture. In addition, GMiner solves the workload skewness problem from which the existing parallel methods suffer; as a result, its performance increases almost linearly as the number of GPUs increases. Through extensive experiments, we demonstrate that GMiner significantly outperforms other representative sequential and parallel methods in most cases, by orders of magnitude on the tested benchmarks. © 2018 The Authors
- Elsevier Inc.
- Related Researcher
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