A Fast Audit Doubt Finding Model. Issue 4 (April 2021)

• Record Type: Journal Article
• Authors: Wan, Quan; Li, Weibo; Wang, Hairong; Yan, Hua; Xiang, Rui
• Is Part Of: Journal of physics. Volume 1865:Issue 4(2021)
• Publication Date: 2021-04
• Subjects: Audit doubts -- outliers -- cluster analysis -- K-means++ -- LOF; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1865/4/042065
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In order to improve the audit efficiency and accuracy under the condition of big data, an audit doubt discovery model (CLOWF) based on an adaptive clustering outlier detection algorithm is proposed. The model first uses the slope ratio method to obtain the K value of the K-means++ algorithm, thereby effectively ensuring the clustering effect; At the same time, for the problem of slow pruning speed, a parallel calculation method for the data radius and centroid of each cluster is proposed and designed; Combined with the weighted local outlier factor detection algorithm to calculate the outlier factor, the data greater than the epsilon threshold of outlier is proposed as an indicator to determine the audit doubt. Through auditing application cases, comparing the local outlier factor detection algorithm and the cluster-based outlier detection algorithm, the results show that the execution time of the CLOWF model is the shortest and the accuracy rate is 97.3%.