Cited 0 time in
Cited 0 time in
SSDMiner: A Scalable and Fast Disk-Based Frequent Pattern Miner
- SSDMiner: A Scalable and Fast Disk-Based Frequent Pattern Miner
- Chon, Kang Wook; Kim, Min Soo
- DGIST Authors
- Kim, Min Soo
- Issue Date
- 7th International Conference on Emerging Databases: Technologies, Applications, and Theory, EDB 2017, 99-110
- 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. © 2018, Springer Nature Singapore Pte Ltd.
- Springer Verlag
- Related Researcher
Big Data Systems; Big Data Mining & Machine Learning; Big Data Bioinformatics; 데이터 마이닝 및 빅데이터 분석; 바이오인포메틱스 및 뉴로인포메틱스; 뇌-기계 인터페이스(BMI)
There are no files associated with this item.
- Department of Information and Communication EngineeringInfoLab2. Conference Papers
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.