数据库查询方法审计疑点发现依赖于审计人员先验知识,当经验不足且审计数据量巨大时,难以发挥大数据优势并从海量数据中发现疑点.为解决这一问题,提出基于改进Leaders算子迭代聚类的审计大数据潜在疑点发现方法.该方法在无先验知识的情形下,通过Leaders算法自动完成审计大数据的初始聚类,在此基础上通过随机抽样融合方法对初始聚类结果优化,最后通过多次迭代聚类的方法,对实例数较少或可疑程度易被掩盖的小簇进一步聚类,实现审计大数据的精确聚类,并将实例较少且行为明显异常的数据聚类识别为潜在疑点,配合审计人员审计经验快速精确定位审计疑点.实验结果验证了算法的有效性,表明算法有助于从海量数据中自主发现审计疑点,缩小疑点筛查范围,提高审计效率.%Database query method audit doubts discovery relies on the prior knowledge of the auditors,but when the auditors have not enough audit experience and the amount of audit data is huge,it is difficult to take advantage of big data and find the audit doubts from the massive data.And so,in order to solve this problem,a method based on improved Leaders operator and iterative clustering is proposed.In the absence of prior knowledge,Leaders algorithm is used for automatic initial clustering of large audit data,and then,the random sampling fusion method is introduced to optimize the clustering results based on that initial clustering center,finally,the multiple iterative clustering method is used to further find the small clusters with fewer or doubtful instances and thus the accurate clustering of large audit data is achieved.The data clusters with fewer instances or obviously abnormal be-havior are identified as potential audit doubts,which can cooperate with audit experience to assist auditors to locate audit doubtful points quickly and accurately.Experimental results verify the effectiveness of the proposed algorithm,and show that the proposed algorithm is helpful to find out the audit doubts from the mass data,narrow the scope of doubts screening and improve the audit efficiency.
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