Abstract:  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 behavior 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.

Key words: computer aided audit, detection of potential audit doubts, improved Leaders operator, data sampling and fusion, iterative clustering