基于非时间属性关联的数据逼真生成算法

Table Data Simulation Generating Algorithm Based on Not-Temporal Attribute

提出基于非时间属性关联的数据逼真生成算法.该算法可以解决数据生成器研发中非时间属性关联构建的困难问题,在大数据测评领域中对仿真数据生成有重要应用价值.首先,从数据集中提取关键的两个非时间属性,对它们分别做两重频数统计.然后,根据两次统计结果计算最大信息系数值来评估相关性,用拉伸指数分布进行拟合,构建出关联模型.最后,通过模型参数构建约束,在此约束的二维矩阵中生成数据.实验结果表明,该算法能够有效地模拟真实数据集的数据特征.

A table data simulation generating algorithm is proposed based on not-temporal attribute correlation. This algorithm can overcome the difficulty in building not-temporal attribute correlation in the development of big data simulation generator, and play an important role in the field of measurement of the big data simulation generated. Firstly,we extract the two key not-temporal attributes from the data set, and make the statistics of twofold frequency. Then, based on the statistical results, we calculate the maximal information coefficient（MIC） value to measure dependence for twovariable relationships. We use the stretched exponential（SE） distribution to fit the relationship, and build the correlation model. Finally, we generate data in a two-dimensional matrix with this model. The experimental results show that this algorithm can effectively describe the data characteristics of the real data set.