Name
Abstract | ||
|---|---|---|
The rapid development of big data puts forward higher requirements on computational efficiency and storage capacity. But the traditional mining algorithms based on classical computing were unable to satisfy the demand of data analysis and computing. Because of quantum systems unique ability of hyper parallel computation and hyper acceleration, the large-scale computing and data storage can be solve well. Although the development of quantum computer has not yet achieved a qualitative leap, quantum computing in big data application has achieved lots of theoretical achievements. In this paper, we make quantum data mining as a starting point. Since we have no sufficient space to store candidate sets of association rules and have low computation ability in the processing of association rules, we propose a novel quantum method of data storage and retrieval for association rules mining based on Boolean matrix, we propose the algorithm called Q-Eclat can accelerate the computation of candidate sets support. According to our analysis based on open data source, the proposed method outperforms the classical Eclat algorithm in terms of storage capacity and computing ability. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BIGCOM.2017.33 | 2017 3rd International Conference on Big Data Computing and Communications (BIGCOM) |
Keywords | Field | DocType |
Quantum Computing,Quantum Storage,Association Rules,Grover algorithm,Q-Eclat algorithm | Quantum,Data mining,Logical matrix,Computer data storage,Computer science,Quantum computer,Theoretical computer science,Association rule learning,Grover's algorithm,Big data,Computation | Conference |
ISBN | Citations | PageRank |
978-1-5386-3350-2 | 0 | 0.34 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiang Gao | 1 | 0 | 0.34 |
| Fengli Zhang | 2 | 154 | 26.40 |
| Rui-jin Wang | 3 | 6 | 1.41 |
| Fan Zhou | 4 | 101 | 23.20 |