贝叶斯粗糙集云数据深度融合算法

Cloud Data Deeply Fusion Algorithm Based on Bayesian Rough Set

云计算环境下,需要对云数据特征进行深度融合,提高对云数据的调度和决策能力。传统的云数据融合算法采用置信增益概率分配算法,当云数据出现多重特征时,融合深度不够,信息提取效果不好。提出一种基于贝叶斯粗糙集的云数据深度融合算法。引入了置信增益函数贝叶斯粗糙集,得到贝叶斯粗糙集云数据模型构建,在特征空间关系中进行特征合并,进行决策表决策属性分区处理,提高融合精度,依据信任函数最大化原则确定新对象的决策属性取值,实现云数据深度融合算法改进。仿真实验表明,采用该算法,能有效提高数据融合深度和精度,稳健性较好,可以明显的抑制噪声的影响,并提高20 d B左右的特征空间增益,算法在高维空间中仍体现出了较为明显的数据融合优势,该算法在云计算和云数据信息处理等领域具有较好应用前景。

Computing environment, the need for cloud data characteristics of the depth of integration, improve the cloud da-ta scheduling and decision making ability. Traditional data fusion use confidence gain probability assignment algorithm cloud, when multiple feature fusion cloud data, not enough depth, the effect of information extraction is not good. This paper proposed a fusion method for cloud data depth based on Bayesian Rough sets. Introduce a confidence gain function Bayes-ian Rough set, obtained the Bayesian Rough Set cloud data model, feature combination in the spatial relationship feature, decision table decision attribute partition processing, to improve the fusion accuracy synthesis results after data fusion based on trust function maximum principle to determine the decision attribute values the new object, implementation of cloud data depth improvement fusion algorithm. Simulation results show that using this algorithm can effectively improve the data fusion depth and precision, good stability, can suppress the noise effect obviously, and improve the feature space gain about 20 dB algorithm in high dimension space, still reflects the obvious advantages of data fusion, the algorithm will be in the cloud computing and cloud data information processing and other fields wide application prospect.