基于云计算的多层量子精英属性协同约简算法

针对传统粗糙集属性约简算法无法高效处理日益增长的大数据问题，提出了一种基于云计算的多层量子精英属性协同约简算法。该算法首先在云计算MapReduce模型下将大规模数据集划分到不同的进化蛙群中，分别获得各子种群最优解；然后构造一种基于多层量子蛙群精英向量的属性协同约简策略，挑选出具有全局搜索和局部精化最强优化能力的精英子种群向量，快速引导各子种群找到各自最小属性约简集，从而取得大规模数据集的全局最优属性约简集。实验结果表明本文算法在大规模数据集上求解全局最优属性约简解的效率和精度具有明显优势，同时应用于电子病历数据库MRI分割效果表明其具有较强适用性。

Attribute Co-reduction Algorithm based on Cloud Computing and Multi-layers Quantum Elitists

In order to solve out the knowledge reduction task for the explosive increment big data, a novel attribute co-reduction algorithm (CMQEACR) based on cloud computing and multi-layer quantum elitists was proposed in this paper. First, the large-scale dataset was decomposed into different evolutionary frog subpopulations under the MapReduce cloud mechanism, and the optimal solutions of subpopulations were attained, respectively. Second, the strategy of attribute co-reduction based on the elitist vectors of multi-layer quantum frogs was constructed, and the vectors of elitist subpopulations with the strongest optimization ability of both global searching and local exploration ware selected out. This strategy could guide each subpopulation to obtain its respective minimum attribute reduction. So the global optimal reduction set could be achieved efficiently. The experimental results indicated the effectiveness and accuracy of proposed algorithm for attribute reduction on big data, compared with the representative methods. Meanwhile, the proposed algorithm was used for the segmentation of MRI in the electronic medical record database and the promising results showed its better applicability.