Name
Abstract | ||
|---|---|---|
According to the complexity of ocean data, this paper adopts a parallel mining algorithm of association rules to explore the correlation and regularity of oxygen, temperature, phosphate, nitrate and silicate in the ocean. After the marine data is interpolated, this paper utilizes the parallel FP-growth algorithm to mine the data and then briefly analyzes the mining results of the frequent itemsets and association rules. The relationship between the parallel efficiency and the core number of CPU is analyzed through datasets with different scales. The experimental results indicate that the acceleration effect is ideal when each thread scored 200,000–300,000 data, which leads to more than 1.2 times of performance improvement. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11227-018-2297-6 | The Journal of Supercomputing |
Keywords | Field | DocType |
Association rules mining, FP-growth, WOA13, Parallel algorithm | Parallel algorithm,Computer science,World Ocean Atlas,Interpolation,Parallel computing,Algorithm,Thread (computing),Association rule learning,Acceleration,Multi-core processor,Performance improvement | Journal |
Volume | Issue | ISSN |
75 | 2 | 1573-0484 |
Citations | PageRank | References |
2 | 0.38 | 12 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Jiang | 1 | 2 | 1.05 |
| Minghao Zhao | 2 | 4 | 1.44 |
| Chengquan Hu | 3 | 13 | 7.05 |
| Lili He | 4 | 2 | 0.38 |
| Hongtao Bai | 5 | 14 | 4.70 |
| jin wang | 6 | 243 | 36.79 |