优先关联的Web日志数据逼真生成算法

Simulate Generating Web Log Algorithm Using Fields' Priority Relevance

字段关联的构建方法是Web数据逼真生成中的困难问题.提出一种基于MIC的字段优先关联的Web数据逼真生成算法.该算法与现有的方法完全不同:首先,提取真实Web日志数据集中相应字段间的MIC系数;然后,结合字段的重尾特性,采用SE分布对字段的重尾性进行建模;最后,建立字段关联模型,模拟出真实数据集中的字段间依赖性,从而逼真生成目标数据集.实验表明,生成的数据集能够保持合理的字段间的均衡性以及节点间的相似性.

The construction method of field relevance is a difficult problem in the Web data generation. A new algorithm for fields＇ priority relevance based on maximal information coefficient is proposed. The algorithm is completely different from the existing method. Firstly, the maximal information coefficient between the appropriate fields needs to be extracted from real Web log data. Then, combined with the field of heavy tailed characteristics, the field is modeled by stretched exponential distribution. Finally, real data＇s field dependence is simulated by the fields＇ relevance model, so as to generate a realistic target data set. The experiments show that the generated data sets can maintain a reasonable balance between the fields and the similarity between the nodes.